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Your consent applies to the following domains: dl.acm.org Cookie declaration last updated on 30.04.24 by Cookiebot skip to main content * Advanced Search * Browse * About * * Sign in * Register * Search ACM Digital Library SearchSearch * Advanced Search * Journals * Magazines * Proceedings * Books * SIGs * Conferences * People * * Browse * About * More * Search ACM Digital Library SearchSearch Advanced Search ACM Computing Surveys * Journal Home * Just Accepted * Latest Issue * * Archive * Authors * Author Guidelines * Calls for Papers * Submission Site * ACM Author Policies * Editors * Editorial Board * Associate Editor Guidelines * Associate Editors Welcome Video * Reviewers * Reviewer Guidelines * About * Charter * Announcements * Abstracting/Indexing * CSUR Author List * CSUR Affiliations * ACM Award Winners * Contact Us * More * Home * ACM Journals * ACM Computing Surveys * Vol. 40, No. 1 * Survey of graph database models research-article Share on * * * * * SURVEY OF GRAPH DATABASE MODELS * Authors: * Renzo Angles Universidad de Chile, Santiago, Chile Universidad de Chile, Santiago, Chile View Profile , * Claudio Gutierrez Universidad de Chile, Santiago, Chile Universidad de Chile, Santiago, Chile View Profile Authors Info & Claims ACM Computing SurveysVolume 40Issue 1Article No.: 1pp 1–39https://doi.org/10.1145/1322432.1322433 Published:22 February 2008Publication History * 633citation * 17,463 * Downloads Metrics Total Citations633 Total Downloads17,463 Last 12 Months882 Last 6 weeks107 * Get Citation Alerts * * * Publisher Site * * Get Access ACM COMPUTING SURVEYS Volume 40, Issue 1 PreviousArticleNextArticle * * * * Abstract * References * Cited By * Index Terms * Recommendations * Comments ACM COMPUTING SURVEYS Volume 40, Issue 1 PreviousArticleNextArticle * * * * Abstract * References * Cited By * Index Terms * Recommendations * Comments * * * * 136References * * * Skip Abstract Section ABSTRACT Graph database models can be defined as those in which data structures for the schema and instances are modeled as graphs or generalizations of them, and data manipulation is expressed by graph-oriented operations and type constructors. These models took off in the eighties and early nineties alongside object-oriented models. Their influence gradually died out with the emergence of other database models, in particular geographical, spatial, semistructured, and XML. Recently, the need to manage information with graph-like nature has reestablished the relevance of this area. The main objective of this survey is to present the work that has been conducted in the area of graph database modeling, concentrating on data structures, query languages, and integrity constraints. REFERENCES 1. Abiteboul, S. 1997. Querying semi-structured data. In Proceedings of the 6th International Conference on Database Theory (ICDT). LNCS, vol. 1186. Springer, 1--18. Google ScholarDigital Library 2. Abiteboul, S. and Hull, R. 1984. IFO: A formal semantic database model. In Proceedings of the 3th Symposium on Principles of Database Systems (PODS). ACM Press, 119--132. Google ScholarDigital Library 3. Abiteboul, S., Quass, D., McHugh, J., Widom, J., and Wiener, J. L. 1997. The Lorel query language for semistructured data. Int. J. Dig. Libr. 1, 1, 68--88.Google ScholarCross Ref 4. Abiteboul, S. and Vianu, V. 1997. Queries and computation on the Web. In Proceedings of the 6th International Conference on Database Theory (ICDT). LNCS, vol. 1186. Springer, 262--275. Google ScholarDigital Library 5. Agrawal, R. and Jagadish, H. V. 1988. Efficient search in very large databases. In Proceedings of the 14th International Conference on Very Large Data Bases (VLDB). Morgan Kaufmann, 407--418. Google ScholarDigital Library 6. Agrawal, R. and Jagadish, H. V. 1989. Materialization and incremental update of path information. In Proceedings of the 5th International Conference on Data Engineering (ICDE). IEEE Computer Society, 374--383. Google ScholarDigital Library 7. Agrawal, R. and Jagadish, H. V. 1994. Algorithms for searching massive graphs. IEEE Trans. Knowl. Data Eng. 6, 2, 225--238. Google ScholarDigital Library 8. Albert, R. and Barabási, A.-L. 2002. Statistical mechanics of complex networks. Rev. Mod. Phy. 74, 47.Google ScholarCross Ref 9. Alechina, N., Demri, S., and de Rijke, M. 2003. A modal perspective on path constraints. J. Logic Computation 13, 6, 939--956.Google ScholarCross Ref 10. Amann, B. and Scholl, M. 1992. Gram: A Graph Data Model and Query Language. In European Conference on Hypertext Technology (ECHT). ACM, 201--211. Google ScholarDigital Library 11. Andries, M. and Engels, G. 1993. A hybrid query language for an extended entity-relationship model. Tech. Rep. TR 93-15, Institute of Advanced Computer Science, Universiteit Leiden. May.Google Scholar 12. Andries, M., Gemis, M., Paredaens, J., Thyssens, I., and den Bussche, J. V. 1992. Concepts for graph-oriented object manipulation. In Proceedings of the 3rd International Conference on Extending Database Technology (EDBT). LNCS, vol. 580. Springer, 21--38. Google ScholarDigital Library 13. Angles, R. and Gutierrez, C. 2005. Querying RDF data from a graph database perspective. In Proceedings of the 2nd European Semantic Web Conference (ESWC). Number 3532 in LNCS. 346--360. Google ScholarDigital Library 14. Aufaure-Portier, M.-A. and Trépied, C. 1976. A survey of query languages for geographic information systems. In Proceedings of the 3rd International Workshop on Interfaces to Databases. 431--438.Google Scholar 15. Azmoodeh, M. and Du, H. 1988. GQL, A graphical query language for semantic databases. In Proceedings of the 4th International Conference on Scientific and Statistical Database Management (SSDBM). LNCS, vol. 339. Springer, 259--277. Google ScholarDigital Library 16. Beeri, C. 1988. Data models and languages for databases. In Proceedings of the 2nd International Conference on Database Theory (ICDT). LNCS, vol. 326. Springer, 19--40. Google ScholarDigital Library 17. Benkö, G., Flamm, C., and Stadler, P. F. 2003. A graph-based toy model of chemistry. J. Chem. Inform. Computer Science (JCISD) 43, 1 (Jan), 1085--1093.Google Scholar 18. Berge, C. 1973. Graphs and Hypergraphs. North-Holland, Amsterdam. Google ScholarDigital Library 19. Brandes, U. 2005. Network Analysis. Number 3418 in LNCS. Springer-Verlag.Google Scholar 20. Bray, T., Paoli, J., and Sperberg-McQueen, C. M. 1998. Extensible Markup Language (XML) 1.0, W3C Recommendation 10, (February). http://www.w3.org/TR/1998/REC-xml-19980210. Google ScholarDigital Library 21. Broder, A., Kumar, R., Maghoul, F., Raghavan, P., Rajagopalan, S., Stata, R., Tomkins, A., and Wiener, J. 2000. Graph structure in the Web. In Proceedings of the 9th International World Wide Web conference on Computer Networks: The International Journal of Computer and Telecommunications Networking. North-Holland Publishing Co., 309--320. Google ScholarDigital Library 22. Buneman, P. 1997. Semistructured data. In Proceedings of the 16th Symposium on Principles of Database Systems (PODS). ACM Press, 117--121. Google ScholarDigital Library 23. Buneman, P., Davidson, S., Hillebrand, G., and Suciu, D. 1996. A query language and optimization techniques for unstructured data. SIGMOD Record. 25, 2, 505--516. Google ScholarDigital Library 24. Buneman, P., Fan, W., and Weinstein, S. 1998. Path constraints in semistructured and structured databases. In Proceedings of the 17th Symposium on Principles of Database Systems (PODS). ACM Press, 129-- 138. Google ScholarDigital Library 25. Cardelli, L., Gardner, P., and Ghelli, G. 2002. A spatial logic for querying graphs. In Proceedings of the 29th International Colloquium on Automata, Languages, and Programming (ICALP). LNCS. Springer, 597--610. Google ScholarDigital Library 26. Chandra, A. K. 1988. Theory of database queries. In Proceedings of the 7th Symposium on Principles of Database Systems (PODS). ACM Press, 1--9. Google ScholarDigital Library 27. Chen, P. P.-S. 1976. The entity-relationship model---toward a unified view of data. ACM Trans. Database Syst. 1, 1, 9--36. Google ScholarDigital Library 28. Chomicki, J. 1994. Temporal query languages: a survey. In Proceedings of the First International Conference on Temporal Logic (ICTL). Springer-Verlag, 506--534. Google ScholarDigital Library 29. Codd, E. F. 1970. A relational model of data for large shared data banks. Commun. ACM 13, 6, 377-- 387. Google ScholarDigital Library 30. Codd, E. F. 1980. Data models in database management. In Proceedings of the 1980 Workshop on Data abstraction, Databases, and Conceptual Modeling. ACM Press, 112--114. Google ScholarDigital Library 31. Codd, E. F. 1983. A relational model of data for large shared data banks. Commun. ACM 26, 1, 64--69. Google ScholarDigital Library 32. Conklin, J. 1987. Hypertext: An introduction and survey. IEEE Comput. 20, 9, 17--41. Google ScholarDigital Library 33. Consens, M. and Mendelzon, A. 1993. Hy+: A hygraph-based query and visualization system. SIGMOD Record 22, 2, 511--516. Google ScholarDigital Library 34. Consens, M. P. and Mendelzon, A. O. 1989. Expressing structural hypertext queries in graphlog. In Proceedings of the 2th Conference on Hypertext. ACM Press, 269--292. Google ScholarDigital Library 35. Cruz, I. F., Mendelzon, A. O., and Wood, P. T. 1987. A graphical query language supporting recursion. In Proceedings of the Association for Computing Machinery Special Interest Group on Management of Data. ACM Press, 323--330. Google ScholarDigital Library 36. Cruz, I. F., Mendelzon, A. O., and Wood, P. T. 1989. G+: recursive queries without recursion. In Proceedings of the 2th International Conference on Expert Database Systems (EDS). Addison-Wesley, 645-- 666.Google Scholar 37. Date, C. J. 1981. Referential integrity. In Proceedings of the 7th International Conference on Very Large Data Bases (VLDB). IEEE Computer Society, 2--12. Google ScholarDigital Library 38. de S. Price, D. J. 1965. Networks of scientific papers. Science 149, 510--515.Google ScholarCross Ref 39. Deng, Y. and Chang, S.-K. 1990. A G-Net model for knowledge representation and reasoning. IEEE Trans. Knowl. Data Eng. 2, 3 (Dec), 295--310. Google ScholarDigital Library 40. Deville, Y., Gilbert, D., van Helden, J., and Wodak, S. J. 2003. An overview of data models for the analysis of biochemical pathways. In Proceedings of the First International Workshop on Computational Methods in Systems Biology. Springer-Verlag, 174. Google ScholarDigital Library 41. Dorogovtsev, S. N. and Mendes, J. F. F. 2003. Evolution of Networks---From Biological Nets to the Internet and WWW. Oxford University Press. Google ScholarDigital Library 42. Fernández, M., Florescu, D., Kang, J., Levy, A., and Suciu, D. 1998. Catching the boat with strudel: experiences with a Web-site management system. In Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data. ACM Press, 414--425. Google ScholarDigital Library 43. Flesca, S. and Greco, S. 1999. Partially ordered regular languages for graph queries. In Proceedings of the 26th International Colloquium on Automata, Languages and Programming (ICALP). LNCS, vol. 1644. Springer, 321--330. Google ScholarDigital Library 44. Flesca, S. and Greco, S. 2000. Querying graph databases. In Proceedings of the 7th International Conference on Extending Database Technology---Advances in Database Technology (EDBT). LNCS, vol. 1777. Springer, 510--524. Google ScholarDigital Library 45. Florescu, D., Levy, A., and Mendelzon, A. O. 1998. Database techniques for the World-Wide Web: A survey. SIGMOD Record 27, 3, 59--74. Google ScholarDigital Library 46. Fry, J. P. and Sibley, E. H. 1976. Evolution of data-base management systems. ACM Comput. Surv. 8, 1. Google ScholarDigital Library 47. Furche, T., Linse, B., Bry, F., Plexousakis, D., and Gottlob, G. 2006. RDF querying: language constructs and evaluation methods compared. In Reasoning Web. Number 4126 in LNCS. 1--52.Google Scholar 48. Gemis, M. and Paredaens, J. 1993. An object-oriented pattern matching language. In Proceedings of the First JSSST International Symposium on Object Technologies for Advanced Software. Springer-Verlag, 339--355. Google ScholarDigital Library 49. Gemis, M., Paredaens, J., Thyssens, I., and den Bussche, J. V. 1993. GOOD: A graph-oriented object database system. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data. ACM Press, 505--510. Google ScholarDigital Library 50. Giugno, R. and Shasha, D. 2002. GraphGrep: A fast and universal method for querying graphs. In Proceedings of the IEEE International Conference in Pattern recognition (ICPR).Google Scholar 51. Graves, M. Graph data models for genomics. http://www.xweave.com/people/in graaves/pubs.Google Scholar 52. Graves, M. 1993. Theories and tools for designing application-specific knowledge base data models. Ph.D. dissertation, University of Michigan. Google ScholarDigital Library 53. Graves, M., Bergeman, E. R., and Lawrence, C. B. 1994. Querying a genome database using graphs. In Proceedings of the 3th International Conference on Bioinformatics and Genome Research.Google Scholar 54. Graves, M., Bergeman, E. R., and Lawrence, C. B. 1995a. A graph-theoretic data model for genome mapping databases. In Proceedings of the 28th Hawaii International Conference on System Sciences (HICSS). IEEE Computer Society, 32. Google ScholarDigital Library 55. Graves, M., Bergeman, E. R., and Lawrence, C. B. 1995b. Graph database systems for genomics. IEEE Eng. Medicine Biol. Special issue on Managing Data for the Human Genome Project 11, 6.Google Scholar 56. Griffith, R. L. 1982. Three principles of representation for semantic networks. ACM Trans. Database Syst. 7, 3, 417--442. Google ScholarDigital Library 57. Guha, R.V., Lassila, O., Miller, E., and Brickley, D. 1998. Enabling inferencing. The Query Languages Workshop (QL).Google Scholar 58. Gutiérrez, A., Pucheral, P., Steffen, H., and Thévenin, J.-M. 1994. Database graph views: A practical model to manage persistent graphs. In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB). Morgan Kaufmann, 391--402. Google ScholarDigital Library 59. Güting, R. H. 1994. GraphDB: modeling and querying graphs in databases. In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB). Morgan Kaufmann, 297--308. Google ScholarDigital Library 60. Gyssens, M., Paredaens, J., den Bussche, J. V., and Gucht, D. V. 1990a. A graph-oriented object database model. In Proceedings of the 9th Symposium on Principles of Database Systems (PODS). ACM Press, 417--424. Google ScholarDigital Library 61. Gyssens, M., Paredaens, J., den Bussche, J. V., and Gucht, D. V. 1991. A graph-oriented object database model. Tech. Rep. 91-27, University of Antwerp (UIA), Belgium. (March).Google Scholar 62. Gyssens, M., Paredaens, J., and Gucht, D. V. 1990b. A graph-oriented object model for database end-user interfaces. In Proceedings of the 1990 ACM SIGMOD International Conference on Management of Data. ACM Press, 24--33. Google ScholarDigital Library 63. Hammer, J. and Schneider, M. 2004. The GenAlg project: developing a new integrating data model, language, and tool for managing and querying genomic information. SIGMOD Record 33, 2, 45--50. Google ScholarDigital Library 64. Hammer, M. and McLeod, D. 1978. The semantic data model: a modelling mechanism for data base applications. In Proceedings of the 1978 ACM SIGMOD International Conference on Management of Data. ACM, 26--36. Google ScholarDigital Library 65. Hanneman, R. A. 2001. Introduction to social network methods. Tech. Rep., Department of Sociology, University of California, Riverside.Google Scholar 66. Hayes, J. and Gutierrez, C. 2004. Bipartite graphs as intermediate model for RDF. In Proceedings of the 3th International Semantic Web Conference (ISWC). Number 3298 in LNCS. Springer-Verlag, 47--61.Google Scholar 67. Heuer, A. and Scholl, M. H. 1991. Principles of object-oriented query languages. In Datenbanksysteme in Büro, Technik und Wissenschaft (BTW). Informatik-Fachberichte, vol. 270. Springer, 178--197.Google Scholar 68. Hidders, J. 2001. A graph-based update language for object-oriented data models. Ph.D. dissertation, Technische Universiteit Eindhoven.Google Scholar 69. Hidders, J. 2002. Typing graph-manipulation operations. In Proceedings of the 9th International Conference on Database Theory (ICDT). Springer-Verlag, 394--409. Google ScholarDigital Library 70. Hidders, J. and Paredaens, J. 1993. GOAL, A graph-based object and association language. Advances in Database Systems: Implementations and Applications, CISM, 247--265.Google Scholar 71. Hull, R. and King, R. 1987. Semantic database modeling: Survey, applications, and research issues. ACM Comput. Surv. 19, 3, 201--260. Google ScholarDigital Library 72. ISO. 1999. International Standard ISO/IEC 13250 Topic Maps.Google Scholar 73. Jagadish, H. V. and Olken, F. 2003. Data management for the biosciences: report of the NLM Workshop on Data Management for Molecular and Cell Biology. Tech. Rep. LBNL-52767, National Library of Medicine.Google Scholar 74. Kerschberg, L., Klug, A. C., and Tsichritzis, D. 1976. A taxonomy of data models. In Proceedings of Systems for Large Data Bases (VLDB). North Holland and IFIP, 43--64. Google ScholarDigital Library 75. Kiesel, N., Schurr, A., and Westfechtel, B. 1996. GRAS: A graph-oriented software engineering database system. In IPSEN Book. 397--425. Google ScholarDigital Library 76. Kifer, M., Kim, W., and Sagiv, Y. 1992. Querying object-oriented databases. In Proceedings of the 1992 ACM SIGMOD International Conference on Management of Data. ACM Press, 393--402. Google ScholarDigital Library 77. Kim, W. 1990. Object-oriented databases: definition and research directions. IEEE Trans. Knowl. Data Eng. 2, 3, 327--341. Google ScholarDigital Library 78. Klyne, G. and Carroll, J. 2004. Resource description framework (RDF) concepts and abstract syntax. http://www.w3.org/TR/2004/REC-rdf-concepts-20040210/.Google Scholar 79. Kumar, R., Raghavan, P., Rajagopalan, S., Sivakumar, D., Tomkins, A., and Upfal, E. 2000. The Web as a graph. In Proceedings of the 19th Symposium on Principles of Database Systems (PODS). ACM Press, 1--10. Google ScholarDigital Library 80. Kunii, H. S. 1987. DBMS with graph data model for knowledge handling. In Proceedings of the 1987 Fall Joint Computer Conference on Exploring technology: Today and Tomorrow. IEEE Computer Society Press, 138--142. Google ScholarDigital Library 81. Kuper, G. M. and Vardi, M. Y. 1984. A new approach to database logic. In Proceedings of the 3th Symposium on Principles of Database Systems (PODS). ACM Press, 86--96. Google ScholarDigital Library 82. Kuper, G. M. and Vardi, M. Y. 1993. The Logical Data Model. ACM Trans. Database Syst. 18, 3, 379-- 413. Google ScholarDigital Library 83. Langou, B. and Mainguenaud, M. 1994. Graph data model operations for network facilities in a geographical information system. In Proceedings of the 6th International Symposium on Spatial Data Handling. Vol. 2. 1002--1019.Google Scholar 84. Lécluse, C., Richard, P., and Vélez, F. 1988. O2, an object-oriented data model. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM Press, 424--433. Google ScholarDigital Library 85. Levene, M. and Loizou, G. 1995. A graph-based data model and its ramifications. IEEE Trans. Knowl. Data Eng. 7, 5, 809--823. Google ScholarDigital Library 86. Levene, M. and Poulovassilis, A. 1990. The Hypernode model and its associated query language. In Proceedings of the 5th Jerusalem Conference on Information technology. IEEE Computer Society Press, 520--530. Google ScholarDigital Library 87. Levene, M. and Poulovassilis, A. 1991. An object-oriented data model formalised through hypergraphs. Data Knowl. Eng. 6, 3, 205--224. Google ScholarDigital Library 88. Mainguenaud, M. 1992. Simatic XT: A data model to deal with multi-scaled networks. Comput. Environ. Urban Syst. 16, 281--288.Google ScholarCross Ref 89. Mainguenaud, M. 1995. Modelling the network component of geographical information systems. Int. J. Geog. Inform. Syst. 9, 6, 575--593.Google ScholarCross Ref 90. Mannino, M. V. and Shapiro, L. D. 1990. Extensions to query languages for graph traversal problems. IEEE Trans. Knowl. Data Eng. 2, 3, 353--363. Google ScholarDigital Library 91. McGee, W. C. 1976. On user criteria for data model evaluation. ACM Trans. Database Syst. 1, 4, 370--387. Google ScholarDigital Library 92. McGuinness, D. L. and van Harmelen, F. 2004. OWL Web ontology language overview, W3C recommendation 10 (February). http://www.w3.org/TR/2004/REC-owl-features-20040210/.Google Scholar 93. Medeiros, C. B. and Pires, F. 1994. Databases for GIS. SIGMOD Record 23, 1 (March), 107--115. Google ScholarDigital Library 94. Mendelzon, A. O. and Wood, P. T. 1989. Finding regular simple paths in graph databases. In Proceedings of the 15th International Conference on Very Large Data Bases (VLDB). Morgan Kaufmann Publishers Inc., 185--193. Google ScholarDigital Library 95. Navathe, S. B. 1992. Evolution of data modeling for databases. Communications of the ACM 35, 9, 112--123. Google ScholarDigital Library 96. Nejdl, W., Siberski, W., and Sintek, M. 2003. Design issues and challenges for RDF- and schema-based peer-to-peer systems. SIGMOD Record 32, 3, 41--46. Google ScholarDigital Library 97. Newman, M. E. J. 2003. The structure and function of complex networks. SIAM Rev. 45, 2, 167--256.Google ScholarDigital Library 98. Olken, F. 2003. Tutorial on graph data management for biology. IEEE Computer Society Bioinformatics Conference (CSB).Google Scholar 99. Papakonstantinou, Y., Garcia-Molina, H., and Widom, J. 1995. Object exchange across heterogeneous information sources. In Proceedings of the 11th International Conference on Data Engineering (ICDE). IEEE Computer Society, 251--260. Google ScholarDigital Library 100. Paredaens, J. and Kuijpers, B. 1998. Data models and query languages for spatial databases. Data & Knowledge Engineering (DKE) 25, 1--2, 29--53. Google ScholarDigital Library 101. Paredaens, J., Peelman, P., and Tanca, L. 1995. G-Log: A graph-based query language. IEEE Trans. Knowl. Data Eng. 7, 3, 436--453. Google ScholarDigital Library 102. Peckham, J. and Maryanski, F. J. 1988. Semantic data models. ACM Comput. Surv. 20, 3, 153--189. Google ScholarDigital Library 103. Pepper, S. and Moore, G. 2001. XML topic maps (XTM) 1.0---TopicMaps.Org Specification. http://www.topicmaps.org/xtm/1.0/xtm1-20010806.html.Google Scholar 104. Poulovassilis, A. and Hild, S. G. 2001. Hyperlog: A graph-based system for database browsing, querying, and update. IEEE Trans. Knowl. Data Eng. 13, 2, 316--333. Google ScholarDigital Library 105. Poulovassilis, A. and Levene, M. 1994. A nested-graph model for the representation and manipulation of complex objects. ACM Trans. Inform. Syst. 12, 1, 35--68. Google ScholarDigital Library 106. Prud'hommeaux, E. and Seaborne, A. 2005. SPARQL Query Language for RDF, W3C Working Draft 21 July. http://www.w3.org/TR/2005/WD-rdf-sparql-query-20050721/.Google Scholar 107. Ramakrishnan, R. and Ullman, J. D. 1993. A survey of research on deductive database systems. J. Logic Prog. 23, 2, 125--149.Google ScholarCross Ref 108. Roussopoulos, N. and Mylopoulos, J. 1975. Using semantic networks for database management. In Proceedings of the International Conference on Very Large Data Bases (VLDB). ACM, 144--172. Google ScholarDigital Library 109. Samet, H. and Aref, W. G. 1995. Spatial data models and query processing. In Modern Database Systems. 338--360. Google ScholarDigital Library 110. Schewe, K.-D., Thalheim, B., Schmidt, J. W., and Wetzel, I. 1993. Integrity enforcement in object-oriented databases. In Proceedings of the 4th International Workshop on Foundations of Models and Languages for Data and Objects. Google ScholarDigital Library 111. Shasha, D., Wang, J. T. L., and Giugno, R. 2002. Algorithmics and applications of tree and graph searching. In Proceedings of the 21th Symposium on Principles of Database Systems (PODS). ACM Press, 39-- 52. Google ScholarDigital Library 112. Shekhar, S., Coyle, M., Goyal, B., Liu, D.-R., and Sarkar, S. 1997. Data models in geographic information systems. Commun. ACM 40, 4, 103--111. Google ScholarDigital Library 113. Sheng, L., Ozsoyoglu, Z. M., and Ozsoyoglu, G. 1999. A graph query language and its query processing. In Proceedings of the 15th International Conference on Data Engineering (ICDE). IEEE Computer Society, 572--581. Google ScholarDigital Library 114. Sheth, A., Aleman-Meza, B., Arpinar, I. B., Halaschek-Wiener, C., Ramakrishnan, C., Bertram, C., Warke, Y., Avant, D., Arpinar, F. S., Anyanwu, K., and Kochut, K. 2005. Semantic association identification and knowledge discovery for national security applications. J. Database Manag. 16, 1 (Jan-March), 33--53.Google ScholarCross Ref 115. Shipman, D. W. 1981. The functional data model and the data language DAPLEX. ACM Trans. Database Syst. 6, 1, 140--173. Google ScholarDigital Library 116. Silberschatz, A., Korth, H. F., and Sudarshan, S. 1996. Data models. ACM Comput. Surv. 28, 1, 105--108. Google ScholarDigital Library 117. Sowa, J. F. 1976. Conceptual graphs for a database interface. IBM J. Res. Devel. 20, 4, 336--357.Google ScholarDigital Library 118. Sowa, J. F. 1984. Conceptual Structures: Information Processing in Mind and Machine. Reading, MA, Addison-Wesley. Google ScholarDigital Library 119. Sowa, J. F. 1991. Principles of Semantic Networks: Explorations in the Representation of Knowledge. Morgan Kaufmann Publishers.Google Scholar 120. Stein, L. D. and Tierry-Mieg, J. 1999. AceDB: A genome database management system. Comput. Sci. Eng. 1, 3, 44--52.Google ScholarDigital Library 121. Tansel, A., Clifford, J., Gadia, S., Jajodia, S., Segev, A., and Snodgrass, R. T., Eds. 1993. Temporal Databases: Theory, Design, and Implementation. Benjamin-Cummings. Google ScholarDigital Library 122. Taylor, R. W. and Frank, R. L. 1976. CODASYL data-base management systems. ACM Comput. Surv. 8, 1, 67--103. Google ScholarDigital Library 123. Thalheim, B. 1991. Dependencies in Relational Databases. Leipzig, Teubner Verlag.Google Scholar 124. Thalheim, B. 1996. An overview on semantical constraints for database models. In Proceedings of the 6th International Conference Intellectual Systems and Computer Science.Google Scholar 125. Tompa, F. W. 1989. A data model for flexible hypertext database systems. ACM Trans. Inform. Syst. 7, 1, 85--100. Google ScholarDigital Library 126. Tsichritzis, D. C. and Lochovsky, F. H. 1976. Hierarchical data-base management: A survey. ACM Comput. Surv. 8, 1, 105--123. Google ScholarDigital Library 127. Tsvetovat, M., Diesner, J., and Carley, K. 2004. NetIntel: A database for manipulation of rich social network data. Tech. Rep. CMU-ISRI-04-135, Carnegie Mellon University, School of Computer Science, Institute for Software Research International.Google Scholar 128. Tuv, E., Poulovassilis, A., and Levene, M. 1992. A storage manager for the hypernode model. In Proceedings of the 10th British National Conference on Databases. Number 618 in LNCS. Springer-Verlag, 59--77. Google ScholarDigital Library 129. Vardi, M. Y. 1982. The complexity of relational query languages (extended abstract). In Proceedings of the 14th ACM Symposium on Theory of Computing (STOC). ACM Press, 137--146. Google ScholarDigital Library 130. Vassiliadis, P. and Sellis, T. 1999. A survey of logical models for OLAP Databases. SIGMOD Record 28, 4, 64--69. Google ScholarDigital Library 131. Vianu, V. 2003. A Web odyssey: From Codd to XML. SIGMOD Record 32, 2, 68--77. Google ScholarDigital Library 132. Watters, C. and Shepherd, M. A. 1990. A transient hypergraph-based model for data access. ACM Trans. Inform. Syst. 8, 2, 77--102. Google ScholarDigital Library 133. Weddell, G. E. 1992. Reasoning about functional dependencies generalized for semantic data models. ACM Trans. Database Syst. 17, 1, 32--64. Google ScholarDigital Library 134. Wellman, B., Salaff, J., Dimitrova, D., Garton, L., Gulia, M., and Haythornthwaite, C. 1996. Computer networks as social networks: collaborative work,telework, and virtual community. Ann. Rev. Sociol. 22, 213--238.Google ScholarCross Ref 135. Yannakakis, M. 1990. Graph-theoretic methods in database theory. In Proceedings of the 9th Symposium on Principles of Database Systems (PODS). ACM Press, 230--242. Google ScholarDigital Library 136. Zicari, R. 1991. A framework for schema updates in an object-oriented database system. In Proceedings of the 7th International Conference on Data Engineering (ICDE). IEEE Computer Society, 2--13. Google ScholarDigital Library Show All References CITED BY View all 1. Wang B, Li M, Peng Z and Lu W. (2024). Hierarchical attributed graph-based generative façade parsing for high-rise residential buildings. Automation in Construction. 10.1016/j.autcon.2024.105471. 164. (105471). Online publication date: 1-Aug-2024. https://linkinghub.elsevier.com/retrieve/pii/S0926580524002073 2. Remadi A, El Hage K, Hobeika Y and Bugiotti F. (2024). To prompt or not to prompt: Navigating the use of Large Language Models for integrating and modeling heterogeneous data. Data & Knowledge Engineering. 10.1016/j.datak.2024.102313. 152. (102313). Online publication date: 1-Jul-2024. https://linkinghub.elsevier.com/retrieve/pii/S0169023X24000375 3. Buosi S, Timilsina M, Torrente M, Provencio M, Fey D and Nováček V. (2024). Boosting predictive models and augmenting patient data with relevant genomic and pathway information. Computers in Biology and Medicine. 10.1016/j.compbiomed.2024.108398. 174. (108398). Online publication date: 1-May-2024. https://linkinghub.elsevier.com/retrieve/pii/S0010482524004827 4. Sacilotto R, Mathur S and Gonsalves R. Connecting Asset Stores with Graph Databases. SMPTE Motion Imaging Journal. 10.5594/JMI.2024/XBZC2345. 133:2. (28-36). https://ieeexplore.ieee.org/document/10500341/ 5. Currim S, Snodgrass R and Suh Y. (2024). Identifying the Root Causes of DBMS Suboptimality. ACM Transactions on Database Systems. 49:1. (1-40). Online publication date: 31-Mar-2024. https://doi.org/10.1145/3636425 6. Castro-Ospina A, Solarte-Sanchez M, Vega-Escobar L, Isaza C and Martínez-Vargas J. (2024). Graph-Based Audio Classification Using Pre-Trained Models and Graph Neural Networks. Sensors. 10.3390/s24072106. 24:7. (2106). https://www.mdpi.com/1424-8220/24/7/2106 7. Arroyuelo D, Bustos B, Gómez-Brandón A, Hogan A, Navarro G and Reutter J. (2024). Worst-Case-Optimal Similarity Joins on Graph Databases. Proceedings of the ACM on Management of Data. 2:1. (1-26). Online publication date: 12-Mar-2024. https://doi.org/10.1145/3639294 8. Guo Q, Zhang C, Zhang S and Lu J. (2024). Multi-model query languages: taming the variety of big data. Distributed and Parallel Databases. 42:1. (31-71). Online publication date: 1-Mar-2024. https://doi.org/10.1007/s10619-023-07433-1 9. Besta M, Gerstenberger R, Peter E, Fischer M, Podstawski M, Barthels C, Alonso G and Hoefler T. (2023). Demystifying Graph Databases: Analysis and Taxonomy of Data Organization, System Designs, and Graph Queries. ACM Computing Surveys. 56:2. (1-40). Online publication date: 29-Feb-2024. https://doi.org/10.1145/3604932 10. Deforche M, De Vos I, Bronselaer A and De Tré G. (2024). A Hierarchical Orthographic Similarity Measure for Interconnected Texts Represented by Graphs. Applied Sciences. 10.3390/app14041529. 14:4. (1529). https://www.mdpi.com/2076-3417/14/4/1529 11. Jiang Y, Liu J, Ba J, Yap R, Liang Z and Rigger M. Detecting Logic Bugs in Graph Database Management Systems via Injective and Surjective Graph Query Transformation. Proceedings of the 46th IEEE/ACM International Conference on Software Engineering. (1-12). https://doi.org/10.1145/3597503.3623307 12. Mondal R, Ignatova E, Walke D, Broneske D, Saake G and Heyer R. (2024). Clustering graph data: the roadmap to spectral techniques. Discover Artificial Intelligence. 10.1007/s44163-024-00102-x. 4:1. https://link.springer.com/10.1007/s44163-024-00102-x 13. García R and Angles R. Path Querying in Graph Databases: A Systematic Mapping Study. IEEE Access. 10.1109/ACCESS.2024.3371976. 12. (33154-33172). https://ieeexplore.ieee.org/document/10456906/ 14. Ayats H, Cellier P and Ferré S. (2024). Concepts of neighbors and their application to instance-based learning on relational data. International Journal of Approximate Reasoning. 10.1016/j.ijar.2023.109059. 164. (109059). Online publication date: 1-Jan-2024. https://linkinghub.elsevier.com/retrieve/pii/S0888613X23001901 15. Buosi S, Timilsina M, Janik A, Costabello L, Torrente M, Provencio M, Fey D and Nováček V. (2024). Machine learning estimated probability of relapse in early-stage non-small-cell lung cancer patients with aneuploidy imputation scores and knowledge graph embeddings. Expert Systems with Applications: An International Journal. 235:C. Online publication date: 1-Jan-2024. https://doi.org/10.1016/j.eswa.2023.121127 16. Bhattacharyya A and Chakravarty D. (2024). Graph Database: Re-engineering Methodologies Relational to NOSQL Databases. Intelligent Strategies for ICT. 10.1007/978-981-97-1260-1_12. (131-146). https://link.springer.com/10.1007/978-981-97-1260-1_12 17. Swevels A, Fahland D and Montali M. (2024). Implementing Object-Centric Event Data Models in Event Knowledge Graphs. Process Mining Workshops. 10.1007/978-3-031-56107-8_33. (431-443). https://link.springer.com/10.1007/978-3-031-56107-8_33 18. Feng W, Chen S, Liu H and Ji Y. PeeK: A Prune-Centric Approach for K Shortest Path Computation. Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. (1-14). https://doi.org/10.1145/3581784.3607110 19. Besta M, Gerstenberger R, Fischer M, Podstawski M, Blach N, Egeli B, Mitenkov G, Chlapek W, Michalewicz M, Niewiadomski H, Mueller J and Hoefler T. The Graph Database Interface: Scaling Online Transactional and Analytical Graph Workloads to Hundreds of Thousands of Cores. Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. (1-18). https://doi.org/10.1145/3581784.3607068 20. Berkholz C and Nordström J. (2023). Near-optimal Lower Bounds on Quantifier Depth and Weisfeiler–Leman Refinement Steps. Journal of the ACM. 70:5. (1-32). Online publication date: 31-Oct-2023. https://doi.org/10.1145/3195257 21. Das U, Hood C and Nagpure V. (2023). A Reasoning System Architecture for Spectrum Decision Making 2023 International Symposium on Networks, Computers and Communications (ISNCC). 10.1109/ISNCC58260.2023.10323992. 979-8-3503-3559-0. (1-10). https://ieeexplore.ieee.org/document/10323992/ 22. Equi M, Mäkinen V and Tomescu A. (2023). Graphs cannot be indexed in polynomial time for sub-quadratic time string matching, unless SETH fails. Theoretical Computer Science. 975:C. Online publication date: 9-Oct-2023. https://doi.org/10.1016/j.tcs.2023.114128 23. Yuan Y, Soh D, Guo K, Xiong Z and Quek T. (2023). Joint Network Topology Inference via Structural Fusion Regularization. IEEE Transactions on Knowledge and Data Engineering. 35:10. (10351-10364). Online publication date: 1-Oct-2023. https://doi.org/10.1109/TKDE.2023.3264971 24. Chen X and Atlee J. (2023). Variability-aware Neo4j for Analyzing a Graphical Model of a Software Product Line 2023 ACM/IEEE 26th International Conference on Model Driven Engineering Languages and Systems (MODELS). 10.1109/MODELS58315.2023.00034. 979-8-3503-2480-8. (307-318). https://ieeexplore.ieee.org/document/10343738/ 25. Fernau H and Gajjar K. (2023). The Space Complexity of Sum Labelling. Theory of Computing Systems. 67:5. (1026-1049). Online publication date: 1-Oct-2023. https://doi.org/10.1007/s00224-023-10130-2 26. Areces C, Cassano V, Dutto D and Fervari R. Data Graphs with Incomplete Information (and a Way to Complete Them). Logics in Artificial Intelligence. (729-744). https://doi.org/10.1007/978-3-031-43619-2_49 27. Syafiq M, Azri S and Ujang U. (2023). Modelling Reoccurrence of Events in an Event-Based Graph Database for Asset Management 2023 4th International Conference on Artificial Intelligence and Data Sciences (AiDAS). 10.1109/AiDAS60501.2023.10284664. 979-8-3503-1843-2. (102-108). https://ieeexplore.ieee.org/document/10284664/ 28. Deforche M, De Vos I, Bronselaer A and De Tré G. An Orthographic Similarity Measure for Graph-Based Text Representations. Flexible Query Answering Systems. (206-218). https://doi.org/10.1007/978-3-031-42935-4_17 29. Thys G. Developing and implementing a superconnector of producers in the printing industry to facilitate book historical research. Proceedings of the 34th ACM Conference on Hypertext and Social Media. (1-6). https://doi.org/10.1145/3603163.3609061 30. Cotumaccio N, D’Agostino G, Policriti A and Prezza N. (2023). Co-lexicographically Ordering Automata and Regular Languages - Part I. Journal of the ACM. 70:4. (1-73). Online publication date: 31-Aug-2023. https://doi.org/10.1145/3607471 31. Diaz-Ordoñez M, Rodríguez Baena D and Yun-Casalilla B. (2023). A new approach for the construction of historical databases—NoSQL Document-oriented databases: the example of AtlantoCracies . Digital Scholarship in the Humanities. 10.1093/llc/fqad033. 38:3. (1014-1032). Online publication date: 31-Aug-2023. https://academic.oup.com/dsh/article/38/3/1014/7136726 32. Vrgoč D, Rojas C, Angles R, Arenas M, Arroyuelo D, Buil-Aranda C, Hogan A, Navarro G, Riveros C and Romero J. (2023). MillenniumDB: An Open-Source Graph Database System. Data Intelligence. 10.1162/dint_a_00229. 5:3. (560-610). Online publication date: 1-Aug-2023. https://direct.mit.edu/dint/article/5/3/560/117375/MillenniumDB-An-Open-Source-Graph-Database-System 33. Park S and Cheng T. (2023). Framework for constructing multimodal transport networks and routing using a graph database: A case study in London. Transactions in GIS. 10.1111/tgis.13071. 27:5. (1391-1417). Online publication date: 1-Aug-2023. https://onlinelibrary.wiley.com/doi/10.1111/tgis.13071 34. Niknam G, Molaei S, Zare H, Pan S, Jalili M, Zhu T and Clifton D. (2023). DyVGRNN. Neural Networks. 165:C. (596-610). Online publication date: 1-Aug-2023. https://doi.org/10.1016/j.neunet.2023.05.048 35. Equi M, Mäkinen V, Tomescu A and Grossi R. (2023). On the Complexity of String Matching for Graphs. ACM Transactions on Algorithms. 19:3. (1-25). Online publication date: 31-Jul-2023. https://doi.org/10.1145/3588334 36. Sunuwar D and Singh M. (2023). Comparative Analysis of Relational and Graph Databases for Data Provenance: Performance, Queries, and Security Considerations 2023 World Conference on Communication & Computing (WCONF). 10.1109/WCONF58270.2023.10235151. 979-8-3503-1120-4. (1-7). https://ieeexplore.ieee.org/document/10235151/ 37. Walke D, Micheel D, Schallert K, Muth T, Broneske D, Saake G and Heyer R. (2023). The importance of graph databases and graph learning for clinical applications. Database. 10.1093/database/baad045. 2023. Online publication date: 10-Jul-2023. https://academic.oup.com/database/article/doi/10.1093/database/baad045/7222237 38. Hernandez-Bocanegra D and Ziegler J. (2023). Explaining Recommendations through Conversations: Dialog Model and the Effects of Interface Type and Degree of Interactivity. ACM Transactions on Interactive Intelligent Systems. 13:2. (1-47). Online publication date: 30-Jun-2023. https://doi.org/10.1145/3579541 39. Van Houdt B. (2023). On the Cost of Near-Perfect Wear Leveling in Flash-Based SSDs. ACM Transactions on Modeling and Performance Evaluation of Computing Systems. 8:1-2. (1-22). Online publication date: 30-Jun-2023. https://doi.org/10.1145/3576855 40. Boneva I, Groz B, Hidders J, Murlak F and Staworko S. Static Analysis of Graph Database Transformations. Proceedings of the 42nd ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems. (251-261). https://doi.org/10.1145/3584372.3588654 41. Wang G, Yu J, Nguyen M, Zhang Y, Yongchareon S and Han Y. (2023). Motif-based graph attentional neural network for web service recommendation. Knowledge-Based Systems. 269:C. Online publication date: 7-Jun-2023. https://doi.org/10.1016/j.knosys.2023.110512 42. Chen X, Wang B, Jin Z, Feng Y, Li X, Feng X and Liu Q. (2023). Tabby: Automated Gadget Chain Detection for Java Deserialization Vulnerabilities 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). 10.1109/DSN58367.2023.00028. 979-8-3503-4793-7. (179-192). https://ieeexplore.ieee.org/document/10202660/ 43. Olawoyin A, Leung C and Cuzzocrea A. (2023). Evolution of Big Data Models from Hierarchical Models to Knowledge Graphs 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC). 10.1109/COMPSAC57700.2023.00201. 979-8-3503-2697-0. (1325-1330). https://ieeexplore.ieee.org/document/10197017/ 44. Habib S, Khan H, Hamilton-Wright A and Hengartner U. (2023). Revisiting the Security of Biometric Authentication Systems Against Statistical Attacks. ACM Transactions on Privacy and Security. 26:2. (1-30). Online publication date: 31-May-2023. https://doi.org/10.1145/3571743 45. Bhuiyan F and Rahman A. (2023). Log-related Coding Patterns to Conduct Postmortems of Attacks in Supervised Learning-based Projects. ACM Transactions on Privacy and Security. 26:2. (1-24). Online publication date: 31-May-2023. https://doi.org/10.1145/3568020 46. Dambra S, Bilge L and Balzarotti D. (2023). A Comparison of Systemic and Systematic Risks of Malware Encounters in Consumer and Enterprise Environments. ACM Transactions on Privacy and Security. 26:2. (1-30). Online publication date: 31-May-2023. https://doi.org/10.1145/3565362 47. Singh D. (2023). Future Field Systems using Graph Database and IoT 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). 10.1109/ICACITE57410.2023.10182586. 979-8-3503-9926-4. (2183-2186). https://ieeexplore.ieee.org/document/10182586/ 48. Bansal A. HGQL: Supporting Schematic Hypergraphs in GraphQL. Proceedings of the 27th International Database Engineered Applications Symposium. (9-16). https://doi.org/10.1145/3589462.3589481 49. Zhang Y, Liu J, Hou K and Lin Y. (2023). Building a Knowledge Base of Bridge Maintenance Using Knowledge Graph. Advances in Civil Engineering. 10.1155/2023/6047489. 2023. (1-16). Online publication date: 12-Apr-2023. https://www.hindawi.com/journals/ace/2023/6047489/ 50. Di Pierro D, Ferilli S and Redavid D. (2023). LPG-Based Knowledge Graphs: A Survey, a Proposal and Current Trends. Information. 10.3390/info14030154. 14:3. (154). https://www.mdpi.com/2078-2489/14/3/154 51. Aldwairi M, Jarrah M, Mahasneh N and Al-khateeb B. (2023). Graph-based data management system for efficient information storage, retrieval and processing. Information Processing & Management. 10.1016/j.ipm.2022.103165. 60:2. (103165). Online publication date: 1-Mar-2023. https://linkinghub.elsevier.com/retrieve/pii/S0306457322002667 52. Zheng J and Li Y. (2023). Machine learning model of tax arrears prediction based on knowledge graph. Electronic Research Archive. 10.3934/era.2023206. 31:7. (4057-4076). http://www.aimspress.com/article/doi/10.3934/era.2023206 53. Hood C, Nagpure V and Zdunek K. (2023). A Knowledge Graph Approach to Spectrum Explainability. SSRN Electronic Journal. 10.2139/ssrn.4528668. https://www.ssrn.com/abstract=4528668 54. Peng Z, Luo M, Huang W, Li J, Zheng Q, Sun F and Huang J. Learning Representations by Graphical Mutual Information Estimation and Maximization. IEEE Transactions on Pattern Analysis and Machine Intelligence. 10.1109/TPAMI.2022.3147886. 45:1. (722-737). https://ieeexplore.ieee.org/document/9699378/ 55. Aydin A. A Comparative Perspective on Technologies of Big Data Value Chain. IEEE Access. 10.1109/ACCESS.2023.3323160. 11. (112133-112146). https://ieeexplore.ieee.org/document/10281364/ 56. Kaufmann M and Meier A. (2023). NoSQL-Datenbanksysteme. SQL- & NoSQL-Datenbanken. 10.1007/978-3-662-67092-7_7. (251-275). https://link.springer.com/10.1007/978-3-662-67092-7_7 57. Guimarães P, León A and Santos M. (2023). An Automated Patterns-Based Model-to-Model Mapping and Transformation System for Labeled Property Graphs. Research Challenges in Information Science: Information Science and the Connected World. 10.1007/978-3-031-33080-3_11. (171-186). https://link.springer.com/10.1007/978-3-031-33080-3_11 58. Azzi R, Kilany Chamoun R, Serhrouchni A and Sokhn M. (2023). A Healthcare Delivery System Powered by Semantic Data Description and Blockchain. Advances in Information and Communication. 10.1007/978-3-031-28076-4_19. (224-242). https://link.springer.com/10.1007/978-3-031-28076-4_19 59. Kaufmann M and Meier A. (2023). NoSQL Databases. SQL and NoSQL Databases. 10.1007/978-3-031-27908-9_7. (223-244). https://link.springer.com/10.1007/978-3-031-27908-9_7 60. Schweitzer G, Bitzer M and Vielhaber M. (2023). Lifecycle Engineering in the Context of a Medical Device Company – Leveraging MBSE, PLM and AI. Product Lifecycle Management. PLM in Transition Times: The Place of Humans and Transformative Technologies. 10.1007/978-3-031-25182-5_54. (557-566). https://link.springer.com/10.1007/978-3-031-25182-5_54 61. Mertens N, Wohlfahrt T, Hartmann N and Reddy C. (2023). Automatic Transformation of HVAC Diagrams into Machine-Readable Format. Product Lifecycle Management. PLM in Transition Times: The Place of Humans and Transformative Technologies. 10.1007/978-3-031-25182-5_40. (410-419). https://link.springer.com/10.1007/978-3-031-25182-5_40 62. Nai R, Sulis E, Pasteris P, Giunta M and Meo R. (2023). Exploitation and Merge of Information Sources for Public Procurement Improvement. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. 10.1007/978-3-031-23618-1_6. (89-102). https://link.springer.com/10.1007/978-3-031-23618-1_6 63. Liu S and Kim K. (2023). Ontological Knowledge Graph Framework for 4D Printed Product Design: Elongated Homogenous Rod Case. Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus. 10.1007/978-3-031-17629-6_12. (101-109). https://link.springer.com/10.1007/978-3-031-17629-6_12 64. Ding Y, Xu Z, Zhu Q, Li H, Luo Y, Bao Y, Tang L and Zeng S. (2022). Integrated data-model-knowledge representation for natural resource entities. International Journal of Digital Earth. 10.1080/17538947.2022.2047802. 15:1. (653-678). Online publication date: 31-Dec-2022. https://www.tandfonline.com/doi/full/10.1080/17538947.2022.2047802 65. Schäfer J, Tang M, Luu D, Bergmann A and Wiese L. (2022). Graph4Med: a web application and a graph database for visualizing and analyzing medical databases. BMC Bioinformatics. 10.1186/s12859-022-05092-0. 23:1. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-05092-0 66. Kim B, Koo K, Enkhbat U, Kim S, Kim J and Moon B. (2022). M2Bench. Proceedings of the VLDB Endowment. 16:4. (747-759). Online publication date: 1-Dec-2022. https://doi.org/10.14778/3574245.3574259 67. Nguyen Tien T and Nguyen K. Decision graph for timely delivery of multi-AGVs in healthcare environment. Proceedings of the 11th International Symposium on Information and Communication Technology. (186-192). https://doi.org/10.1145/3568562.3568652 68. Al-Zahrani B, Alshehri S, Cherif A and Imine A. (2022). Property Graph Access Control Using View-Based and Query-Rewriting Approaches 2022 IEEE/ACS 19th International Conference on Computer Systems and Applications (AICCSA). 10.1109/AICCSA56895.2022.10017709. 979-8-3503-1008-5. (1-2). https://ieeexplore.ieee.org/document/10017709/ 69. Schuk V, Pombo Jiménez M and Martin U. (2022). Technical specifications to meet the requirements of an Automatic Code Compliance Checking tool and current developments in infrastructure construction. Results in Engineering. 10.1016/j.rineng.2022.100650. 16. (100650). Online publication date: 1-Dec-2022. https://linkinghub.elsevier.com/retrieve/pii/S2590123022003206 70. Niknam G, Molaei S, Zare H, Clifton D and Pan S. (2022). Graph Representation Learning based on Deep Generative Gaussian Mixture Models. Neurocomputing. 10.1016/j.neucom.2022.11.087. Online publication date: 1-Dec-2022. https://linkinghub.elsevier.com/retrieve/pii/S0925231222014916 71. Rani A, Goyal N and Gadia S. (2022). Social data provenance framework based on zero-information loss graph database. Social Network Analysis and Mining. 10.1007/s13278-022-00889-6. 12:1. Online publication date: 1-Dec-2022. https://link.springer.com/10.1007/s13278-022-00889-6 72. Dou H, Mei J, Zhang Y, Chen P and Zheng Z. (2022). JointConf: Jointly autotuning configuration parameters for modularized graph databases. Journal of Software: Evolution and Process. 10.1002/smr.2495. 34:12. Online publication date: 1-Dec-2022. https://onlinelibrary.wiley.com/doi/10.1002/smr.2495 73. Müller C, Bernhard L and Wilhelm D. (2022). Usage of a graph database for the selection of sterile items in the OR. International Journal of Computer Assisted Radiology and Surgery. 10.1007/s11548-022-02795-w. 18:5. (871-875). https://link.springer.com/10.1007/s11548-022-02795-w 74. Saleh D, Kartika Y, Akbar Z, Krisnadhi A and Fatriasari W. On Generating SHACL Shapes from Collective Collection of Plant Trait Data. Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications. (326-330). https://doi.org/10.1145/3575882.3575945 75. Steinmetz D, Merz F, Burmester G, Ma H and Hartmann S. A Modeling Rule for Improving the Performance of Graph Models. Conceptual Modeling. (336-346). https://doi.org/10.1007/978-3-031-17995-2_24 76. Lygerakis F, Kampelis N and Kolokotsa D. (2022). Knowledge Graphs’ Ontologies and Applications for Energy Efficiency in Buildings: A Review. Energies. 10.3390/en15207520. 15:20. (7520). https://www.mdpi.com/1996-1073/15/20/7520 77. Lumsden I, Luettgau J, Lama V, Scully-Allison C, Brink S, Isaacs K, Pearce O and Taufer M. (2022). Enabling Call Path Querying in Hatchet to Identify Performance Bottlenecks in Scientific Applications 2022 IEEE 18th International Conference on e-Science (e-Science). 10.1109/eScience55777.2022.00039. 978-1-6654-6124-5. (256-266). https://ieeexplore.ieee.org/document/9973727/ 78. Ahmed B and Sami N. Simulation System for Producing Real World Dataset to Predict the Covid-19 Contamination Process⋆. Computational Collective Intelligence. (272-282). https://doi.org/10.1007/978-3-031-16014-1_22 79. Santra A, Komar K, Bhowmick S and Chakravarthy S. (2022). From base data to knowledge discovery – A life cycle approach – Using multilayer networks. Data & Knowledge Engineering. 141:C. Online publication date: 1-Sep-2022. https://doi.org/10.1016/j.datak.2022.102058 80. Bender B, Bertheau C, Körppen T, Lauppe H and Gronau N. (2022). A proposal for future data organization in enterprise systems—an analysis of established database approaches. Information Systems and e-Business Management. 20:3. (441-494). Online publication date: 1-Sep-2022. https://doi.org/10.1007/s10257-022-00555-6 81. Bansal A. Hypergraphs as Conflict-Free Partially Replicated Data Types. Database and Expert Systems Applications. (417-432). https://doi.org/10.1007/978-3-031-12423-5_32 82. Altulyan M, Yao L, Wang X, Huang C, Kanhere S and Sheng Q. (2021). A Survey on Recommender Systems for Internet of Things: Techniques, Applications and Future Directions. The Computer Journal. 10.1093/comjnl/bxab049. 65:8. (2098-2132). Online publication date: 11-Aug-2022. https://academic.oup.com/comjnl/article/65/8/2098/6278157 83. Kapsalis P, Kormpakis G, Alexakis K, Karakolis E, Mouzakitis S and Askounis D. (2022). A Reasoning Engine Architecture for Building Energy Metadata Management 2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA). 10.1109/IISA56318.2022.9904419. 978-1-6654-6390-4. (1-7). https://ieeexplore.ieee.org/document/9904419/ 84. Zhang J, Li W, Yuan L, Qin L, Zhang Y and Chang L. (2022). Shortest-path queries on complex networks. Proceedings of the VLDB Endowment. 15:11. (2640-2652). Online publication date: 1-Jul-2022. https://doi.org/10.14778/3551793.3551820 85. Luo R, Nettasinghe B and Krishnamurthy V. Controlling Segregation in Social Network Dynamics as an Edge Formation Game. IEEE Transactions on Network Science and Engineering. 10.1109/TNSE.2022.3162789. 9:4. (2317-2329). https://ieeexplore.ieee.org/document/9743602/ 86. Nahar K and Gill A. (2022). Integrated identity and access management metamodel and pattern system for secure enterprise architecture. Data & Knowledge Engineering. 140:C. Online publication date: 1-Jul-2022. https://doi.org/10.1016/j.datak.2022.102038 87. Kotiranta P, Junkkari M and Nummenmaa J. (2022). Performance of Graph and Relational Databases in Complex Queries. Applied Sciences. 10.3390/app12136490. 12:13. (6490). https://www.mdpi.com/2076-3417/12/13/6490 88. Parnas Gulnes M, Soylu A and Roman D. (2021). A graph-based approach for representing, integrating and analysing neuroscience data: the case of the murine basal ganglia. Data Technologies and Applications. 10.1108/DTA-12-2020-0303. 56:3. (358-381). Online publication date: 22-Jun-2022. https://www.emerald.com/insight/content/doi/10.1108/DTA-12-2020-0303/full/html 89. Angles R, Hogan A, Lassila O, Rojas C, Schwabe D, Szekely P and Vrgoč D. Multilayer graphs. Proceedings of the 5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA). (1-6). https://doi.org/10.1145/3534540.3534696 90. Figueira D, Jez A and Lin A. Data Path Queries over Embedded Graph Databases. Proceedings of the 41st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems. (189-201). https://doi.org/10.1145/3517804.3524159 91. Ilyas I, Rekatsinas T, Konda V, Pound J, Qi X and Soliman M. Saga: A Platform for Continuous Construction and Serving of Knowledge at Scale. Proceedings of the 2022 International Conference on Management of Data. (2259-2272). https://doi.org/10.1145/3514221.3526049 92. Andriamampianina L, Ravat F, Song J and Vallès-Parlangeau N. Querying Temporal Property Graphs. Advanced Information Systems Engineering. (355-370). https://doi.org/10.1007/978-3-031-07472-1_21 93. Reznichenko V. (2022). 60 Years of Databases (part four). PROBLEMS IN PROGRAMMING. 10.15407/pp2022.02.057:2. (57-95). Online publication date: 1-Jun-2022. http://pp.isofts.kiev.ua/ojs1/article/view/500 94. Hogan A, Blomqvist E, Cochez M, D’amato C, Melo G, Gutierrez C, Kirrane S, Gayo J, Navigli R, Neumaier S, Ngomo A, Polleres A, Rashid S, Rula A, Schmelzeisen L, Sequeda J, Staab S and Zimmermann A. (2021). Knowledge Graphs. ACM Computing Surveys. 54:4. (1-37). Online publication date: 31-May-2022. https://doi.org/10.1145/3447772 95. Khan S, Nguyen P, Abdul-Rahman A, Freeman E, Turkay C and Chen M. Rapid Development of a Data Visualization Service in an Emergency Response. IEEE Transactions on Services Computing. 10.1109/TSC.2022.3164146. 15:3. (1251-1264). https://ieeexplore.ieee.org/document/9747990/ 96. Li W, Gao M, Wen D, Zhou H, Ke C and Qin L. (2022). Manipulating Structural Graph Clustering 2022 IEEE 38th International Conference on Data Engineering (ICDE). 10.1109/ICDE53745.2022.00251. 978-1-6654-0883-7. (2749-2761). https://ieeexplore.ieee.org/document/9835630/ 97. Schweitzer G, Mörsdorf S, Bitzer M and Vielhaber M. (2022). Detection of Cause-Effect Relationships in Life Cycle Sustainability Assessment Based on an Engineering Graph. Proceedings of the Design Society. 10.1017/pds.2022.115. 2. (1129-1138). Online publication date: 1-May-2022. https://www.cambridge.org/core/product/identifier/S2732527X22001158/type/journal_article 98. Kuai H, Tao X and Zhong N. (2022). Web Intelligence meets Brain Informatics: Towards the future of artificial intelligence in the connected world. World Wide Web. 25:3. (1223-1241). Online publication date: 1-May-2022. https://doi.org/10.1007/s11280-022-01030-5 99. Sharma C and Sinha R. (2022). FLASc: a formal algebra for labeled property graph schema. Automated Software Engineering. 29:1. Online publication date: 1-May-2022. https://doi.org/10.1007/s10515-022-00336-y 100. Koegl A, Hubwieser P, Talbot M, Krugel J, Striewe M and Goedicke M. (2022). Efficient Structural Analysis of Source Code for Large Scale Applications in Education 2022 IEEE Global Engineering Education Conference (EDUCON). 10.1109/EDUCON52537.2022.9766748. 978-1-6654-4434-7. (24-30). https://ieeexplore.ieee.org/document/9766748/ 101. Kejriwal M. (2022). Knowledge Graphs: A Practical Review of the Research Landscape. Information. 10.3390/info13040161. 13:4. (161). https://www.mdpi.com/2078-2489/13/4/161 102. Cotumaccio N. (2022). Graphs can be succinctly indexed for pattern matching in $O(\vert E\vert ^{2}+\vert V\vert ^{5/2})$ time 2022 Data Compression Conference (DCC). 10.1109/DCC52660.2022.00035. 978-1-6654-7893-9. (272-281). https://ieeexplore.ieee.org/document/9810716/ 103. Ahmadi Z, Parand F and Matinfar F. (2021). A fuzzy logic‐based approach for fuzzy queries over NoSQL graph database . Concurrency and Computation: Practice and Experience. 10.1002/cpe.6542. 34:1. Online publication date: 10-Jan-2022. https://onlinelibrary.wiley.com/doi/10.1002/cpe.6542 104. Marwaha H, Sharma A and Sharma V. (2022). Role of Graph Theory in Computational Neuroscience. Futuristic Design and Intelligent Computational Techniques in Neuroscience and Neuroengineering. 10.4018/978-1-7998-7433-1.ch005. (86-97). http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-7998-7433-1.ch005 105. Yuan Y, Soh D, Yang X, Guo K and Quek T. (2022). GRACGE: Graph Signal Clustering and Multiple Graph Estimation. IEEE Transactions on Signal Processing. 70. (2015-2030). Online publication date: 1-Jan-2022. https://doi.org/10.1109/TSP.2022.3167145 106. Xu Z, Wu H, Chen X, Wang Y and Yue Z. Building a Natural Language Query and Control Interface for IoT Platforms. IEEE Access. 10.1109/ACCESS.2022.3186760. 10. (68655-68668). https://ieeexplore.ieee.org/document/9808139/ 107. Akid H, Frey G, Ayed M and Lachiche N. Performance of NoSQL Graph Implementations of Star vs. Snowflake Schemas. IEEE Access. 10.1109/ACCESS.2022.3171256. 10. (48603-48614). https://ieeexplore.ieee.org/document/9769769/ 108. Sun N, Li C, Chan H, Dung Le B, Islam M, Zhang L, Islam M and Armstrong W. Defining Security Requirements With the Common Criteria: Applications, Adoptions, and Challenges. IEEE Access. 10.1109/ACCESS.2022.3168716. 10. (44756-44777). https://ieeexplore.ieee.org/document/9761799/ 109. Benjamin S, Christopher R and Carl H. (2023). Feature modeling for configurable and adaptable modular buildings. Advanced Engineering Informatics. 51:C. Online publication date: 1-Jan-2022. https://doi.org/10.1016/j.aei.2021.101514 110. Giabelli A, Malandri L, Mercorio F and Mezzanzanica M. (2022). GraphLMI: A data driven system for exploring labor market information through graph databases. Multimedia Tools and Applications. 81:3. (3061-3090). Online publication date: 1-Jan-2022. https://doi.org/10.1007/s11042-020-09115-x 111. Șimonca I, Corbea A and Belciu A. (2022). Analytical Capabilities of Graphs in Oracle Multimodel Database. Education, Research and Business Technologies. 10.1007/978-981-16-8866-9_9. (97-109). https://link.springer.com/10.1007/978-981-16-8866-9_9 112. Ma Z, Li G and Ma R. (2022). RDF Data and Management. Modeling and Management of Fuzzy Semantic RDF Data. 10.1007/978-3-031-11669-8_1. (1-31). https://link.springer.com/10.1007/978-3-031-11669-8_1 113. Natani G and Watanabe S. (2021). Knowledge Graph-based Data Transformation Recommendation Engine 2021 IEEE International Conference on Big Data (Big Data). 10.1109/BigData52589.2021.9671905. 978-1-6654-3902-2. (4617-4623). https://ieeexplore.ieee.org/document/9671905/ 114. Daverio P, Chaudhry H, Margara A and Rossi M. (2021). Temporal Pattern Recognition in Graph Data Structures 2021 IEEE International Conference on Big Data (Big Data). 10.1109/BigData52589.2021.9671837. 978-1-6654-3902-2. (2753-2763). https://ieeexplore.ieee.org/document/9671837/ 115. Purohit S, Van N and Chin G. (2021). Semantic Property Graph for Scalable Knowledge Graph Analytics 2021 IEEE International Conference on Big Data (Big Data). 10.1109/BigData52589.2021.9671547. 978-1-6654-3902-2. (2672-2677). https://ieeexplore.ieee.org/document/9671547/ 116. Zheng X, Xiao Y, Song W, Tong F, Liu S and Zhao D. (2021). COVID19-OBKG: An Ontology-Based Knowledge Graph and Web Service for COVID-19 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 10.1109/BIBM52615.2021.9669535. 978-1-6654-0126-5. (2456-2462). https://ieeexplore.ieee.org/document/9669535/ 117. Qassimi S, Abdelwahed E, Hafidi M and Qazdar A. (2021). Towards a folksonomy graph-based context-aware recommender system of annotated books. Journal of Big Data. 10.1186/s40537-021-00457-3. 8:1. Online publication date: 1-Dec-2021. https://journalofbigdata.springeropen.com/articles/10.1186/s40537-021-00457-3 118. Bollen E, Hendrix R, Kuijpers B and Vaisman A. (2021). Towards the Internet of Water: Using graph databases for hydrological analysis on the Flemish river system. Transactions in GIS. 10.1111/tgis.12801. 25:6. (2907-2938). Online publication date: 1-Dec-2021. https://onlinelibrary.wiley.com/doi/10.1111/tgis.12801 119. Sharma C, Sinha R and Johnson K. (2021). Practical and comprehensive formalisms for modelling contemporary graph query languages. Information Systems. 102:C. Online publication date: 1-Dec-2021. https://doi.org/10.1016/j.is.2021.101816 120. Lourdusamy R and Mattam X. (2021). A Knowledgebase Model Using RDF Knowledge Graph for ClinicalDecision Support Systems. Semantic Web for Effective Healthcare. 10.1002/9781119764175.ch10. (215-247). Online publication date: 23-Nov-2021. https://onlinelibrary.wiley.com/doi/10.1002/9781119764175.ch10 121. Soleymani F and Paquet E. (2021). Deep graph convolutional reinforcement learning for financial portfolio management – DeepPocket. Expert Systems with Applications: An International Journal. 182:C. Online publication date: 15-Nov-2021. https://doi.org/10.1016/j.eswa.2021.115127 122. Hogan A, Blomqvist E, Cochez M, d'Amato C, Melo G, Gutierrez C, Kirrane S, Gayo J, Navigli R, Neumaier S, Ngomo A, Polleres A, Rashid S, Rula A, Schmelzeisen L, Sequeda J, Staab S and Zimmermann A. (2021). Knowledge Graphs. Synthesis Lectures on Data, Semantics, and Knowledge. 10.2200/S01125ED1V01Y202109DSK022. 12:2. (1-257). Online publication date: 8-Nov-2021. https://www.morganclaypool.com/doi/10.2200/S01125ED1V01Y202109DSK022 123. Kim L, Yahia E, Segonds F, Véron P and Mallet A. (2021). i-Dataquest. Computers in Industry. 132:C. Online publication date: 1-Nov-2021. https://doi.org/10.1016/j.compind.2021.103527 124. Hajibabaee P, Malekzadeh M, Heidari M, Zad S, Uzuner O and Jones J. (2021). An Empirical Study of the GraphSAGE and Word2vec Algorithms for Graph Multiclass Classification 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). 10.1109/IEMCON53756.2021.9623238. 978-1-6654-0066-4. (0515-0522). https://ieeexplore.ieee.org/document/9623238/ 125. Hüseynli A and Akcayol M. Bloom Filter Based Graph Database CRUD Optimization for Stream Data. 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). (1056-1061). https://doi.org/10.1109/IDAACS53288.2021.9661054 126. Huang S, Yen H, Liu Y, Tseng K, Kung J, Lin C, Li Y, Chen Y and Wang C. (2021). Cluster Tool Performance Analysis using Graph Database 2021 IEEE 34th International System-on-Chip Conference (SOCC). 10.1109/SOCC52499.2021.9739223. 978-1-6654-2931-3. (230-235). https://ieeexplore.ieee.org/document/9739223/ 127. Fernau H and Gajjar K. The Space Complexity of Sum Labelling. Fundamentals of Computation Theory. (230-244). https://doi.org/10.1007/978-3-030-86593-1_16 128. Figueira D. Foundations of Graph Path Query Languages. Reasoning Web. Declarative Artificial Intelligence . (1-21). https://doi.org/10.1007/978-3-030-95481-9_1 129. Debrouvier A, Parodi E, Perazzo M, Soliani V and Vaisman A. (2021). A model and query language for temporal graph databases. The VLDB Journal — The International Journal on Very Large Data Bases. 30:5. (825-858). Online publication date: 1-Sep-2021. https://doi.org/10.1007/s00778-021-00675-4 130. Mavrogiorgos K, Kiourtis A, Mavrogiorgou A and Kyriazis D. A Comparative Study of MongoDB, ArangoDB and CouchDB for Big Data Storage. Proceedings of the 2021 5th International Conference on Cloud and Big Data Computing. (8-14). https://doi.org/10.1145/3481646.3481648 131. de Anda-Jauregui G and Hernandez-Lemus E. (2021). Identification of Potential Adverse Drug Reactions using Random Walk on Network Models 2021 Mexican International Conference on Computer Science (ENC). 10.1109/ENC53357.2021.9534826. 978-1-6654-2612-1. (1-6). https://ieeexplore.ieee.org/document/9534826/ 132. Fan W, He T, Lai L, Li X, Li Y, Li Z, Qian Z, Tian C, Wang L, Xu J, Yao Y, Yin Q, Yu W, Zhou J, Zhu D and Zhu R. (2021). GraphScope. Proceedings of the VLDB Endowment. 14:12. (2879-2892). Online publication date: 1-Jul-2021. https://doi.org/10.14778/3476311.3476369 133. Nahar K, Gill A and Roach T. (2021). Developing an access control management metamodel for secure digital enterprise architecture modeling. SECURITY AND PRIVACY. 10.1002/spy2.160. 4:4. Online publication date: 1-Jul-2021. https://onlinelibrary.wiley.com/doi/10.1002/spy2.160 134. Angles R, Bonifati A, Dumbrava S, Fletcher G, Hare K, Hidders J, Lee V, Li B, Libkin L, Martens W, Murlak F, Perryman J, Savković O, Schmidt M, Sequeda J, Staworko S and Tomaszuk D. PG-Keys: Keys for Property Graphs. Proceedings of the 2021 International Conference on Management of Data. (2423-2436). https://doi.org/10.1145/3448016.3457561 135. Arenas M, Gutierrez C and Sequeda J. Querying in the Age of Graph Databases and Knowledge Graphs. Proceedings of the 2021 International Conference on Management of Data. (2821-2828). https://doi.org/10.1145/3448016.3457545 136. DEĞERLİ A. (2021). AĞ TOPLUMU YAKLAŞIMI İLE AKADEMİK BİR SOSYAL AĞ MODELİ İÇİN GRAF VERİ TABANI ÖNERİSİ. Beykoz Akademi Dergisi. 10.14514/BYK.m.26515393.2021.9/1.68-88. (68-88). https://dergipark.org.tr/tr/doi/10.14514/BYK.m.26515393.2021.9/1.68-88 137. Ramis Ferrer B, Mohammed W, Ahmad M, Iarovyi S, Zhang J, Harrison R and Martinez Lastra J. (2021). Comparing ontologies and databases: a critical review of lifecycle engineering models in manufacturing. Knowledge and Information Systems. 63:6. (1271-1304). Online publication date: 1-Jun-2021. https://doi.org/10.1007/s10115-021-01558-4 138. Schneider S, Lambers L and Orejas F. (2021). A logic-based incremental approach to graph repair featuring delta preservation. International Journal on Software Tools for Technology Transfer (STTT). 23:3. (369-410). Online publication date: 1-Jun-2021. https://doi.org/10.1007/s10009-020-00584-x 139. Timón-Reina S, Rincón M and Martínez-Tomás R. (2021). An overview of graph databases and their applications in the biomedical domain. Database. 10.1093/database/baab026. 2021. Online publication date: 18-May-2021. https://academic.oup.com/database/article/doi/10.1093/database/baab026/6277712 140. Nicolaescu A, Mastorakis S and Psaras I. Store Edge Networked Data (SEND): A Data and Performance Driven Edge Storage Framework. IEEE INFOCOM 2021 - IEEE Conference on Computer Communications. (1-10). https://doi.org/10.1109/INFOCOM42981.2021.9488804 141. Kuhn M and Franke J. (2021). Data continuity and traceability in complex manufacturing systems: a graph-based modeling approach. International Journal of Computer Integrated Manufacturing. 10.1080/0951192X.2021.1901320. (1-18). https://www.tandfonline.com/doi/full/10.1080/0951192X.2021.1901320 142. Komatani K, Fujioka Y, Nakashima K, Hayashi K and Nakano M. Knowledge Graph Completion-based Question Selection for Acquiring Domain Knowledge through Dialogues. Proceedings of the 26th International Conference on Intelligent User Interfaces. (531-541). https://doi.org/10.1145/3397481.3450653 143. Kashef M, Liu Y, Montgomery K and Candell R. (2020). Wireless Cyber-Physical System Performance Evaluation Through a Graph Database Approach. Journal of Computing and Information Science in Engineering. 10.1115/1.4048205. 21:2. Online publication date: 1-Apr-2021. https://asmedigitalcollection.asme.org/computingengineering/article/doi/10.1115/1.4048205/1086513/Wireless-CyberPhysical-System-Performance 144. Qiao R, Feng K, He H and Zhong X. (2020). Graph Pattern Matching: Capturing Bisimilar Subgraph. International Journal of Pattern Recognition and Artificial Intelligence. 10.1142/S0218001421500117. 35:03. (2150011). Online publication date: 15-Mar-2021. https://www.worldscientific.com/doi/abs/10.1142/S0218001421500117 145. Krommyda M and Kantere V. (2021). Spatial Data Management in IoT Systems: Solutions and Evaluation. International Journal of Semantic Computing. 10.1142/S1793351X21300016. 15:01. (117-139). Online publication date: 1-Mar-2021. https://www.worldscientific.com/doi/abs/10.1142/S1793351X21300016 146. Giabelli A, Malandri L, Mercorio F, Mezzanzanica M and Seveso A. (2021). Skills2Job. Applied Soft Computing. 101:C. Online publication date: 1-Mar-2021. https://doi.org/10.1016/j.asoc.2020.107049 147. Zhou Z, Shi C, Shen X, Cai L, Wang H, Liu Y, Zhao Y and Chen W. Context-aware Sampling of Large Networks via Graph Representation Learning. IEEE Transactions on Visualization and Computer Graphics. 10.1109/TVCG.2020.3030440. 27:2. (1709-1719). https://ieeexplore.ieee.org/document/9224191/ 148. Šestak M, Heričko M, Družovec T and Turkanović M. (2021). Applying k-vertex cardinality constraints on a Neo4j graph database. Future Generation Computer Systems. 10.1016/j.future.2020.09.036. 115. (459-474). Online publication date: 1-Feb-2021. https://linkinghub.elsevier.com/retrieve/pii/S0167739X19324094 149. Rabuzin K and Šestak M. (2021). Graph Database Management Systems. Encyclopedia of Information Science and Technology, Fifth Edition. 10.4018/978-1-7998-3479-3.ch053. (778-790). http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-7998-3479-3.ch053 150. Kattepur A. (2021). Knowledge-Driven Autonomous Robotic Action Planning for Industry 4.0. Innovations in the Industrial Internet of Things (IIoT) and Smart Factory. 10.4018/978-1-7998-3375-8.ch011. (149-171). http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-7998-3375-8.ch011 151. Nadal S, Abello A, Romero O, Vansummeren S and Vassiliadis P. Graph-driven Federated Data Management. IEEE Transactions on Knowledge and Data Engineering. 10.1109/TKDE.2021.3077044. (1-1). https://ieeexplore.ieee.org/document/9422168/ 152. Wu X, Jiang T, Zhu Y and Bu C. Knowledge Graph for China's Genealogy. IEEE Transactions on Knowledge and Data Engineering. 10.1109/TKDE.2021.3073745. (1-1). https://ieeexplore.ieee.org/document/9409650/ 153. Samaraweera G and Chang J. Security and Privacy Implications on Database Systems in Big Data Era: A Survey. IEEE Transactions on Knowledge and Data Engineering. 10.1109/TKDE.2019.2929794. 33:1. (239-258). https://ieeexplore.ieee.org/document/8765784/ 154. Mostajabi F, Safaei A and Sahafi A. A Systematic Review of Data Models for the Big Data Problem. IEEE Access. 10.1109/ACCESS.2021.3112880. 9. (128889-128904). https://ieeexplore.ieee.org/document/9537765/ 155. Ruiz-Saavedra S, García-González H, Arboleya S, Salazar N, Emilio Labra-Gayo J, Díaz I, Gueimonde M, González S and de los Reyes-Gavilán C. (2021). Intestinal microbiota alterations by dietary exposure to chemicals from food cooking and processing. Application of data science for risk prediction. Computational and Structural Biotechnology Journal. 10.1016/j.csbj.2021.01.037. 19. (1081-1091). https://linkinghub.elsevier.com/retrieve/pii/S2001037021000416 156. Ziegler J, Reimann P, Keller F and Mitschang B. (2021). A Metadata Model to Connect Isolated Data Silos and Activities of the CAE Domain. Advanced Information Systems Engineering. 10.1007/978-3-030-79382-1_13. (213-228). https://link.springer.com/10.1007/978-3-030-79382-1_13 157. Ahmed A, Enns K and Thomo A. (2021). Triangle Enumeration for Billion-Scale Graphs in RDBMS. Advanced Information Networking and Applications. 10.1007/978-3-030-75075-6_13. (160-173). https://link.springer.com/10.1007/978-3-030-75075-6_13 158. Jalali A. (2021). Graph-Based Process Mining. Process Mining Workshops. 10.1007/978-3-030-72693-5_21. (273-285). http://link.springer.com/10.1007/978-3-030-72693-5_21 159. Ahmed A and Thomo A. (2021). PageRank for Billion-Scale Networks in RDBMS. Advances in Intelligent Networking and Collaborative Systems. 10.1007/978-3-030-57796-4_9. (89-100). http://link.springer.com/10.1007/978-3-030-57796-4_9 160. Matter H, Buning C, Stefanescu D, Ruf S and Hessler G. (2020). Using Graph Databases to Investigate Trends in Structure–Activity Relationship Networks. Journal of Chemical Information and Modeling. 10.1021/acs.jcim.0c00947. 60:12. (6120-6134). Online publication date: 28-Dec-2021. https://pubs.acs.org/doi/10.1021/acs.jcim.0c00947 161. Ma F, Ma S and Liu Q. (2020). Graphical Probability Model and Heritage Tourism Routine Design 2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C). 10.1109/QRS-C51114.2020.00094. 978-1-7281-8915-4. (535-541). https://ieeexplore.ieee.org/document/9282680/ 162. Rizvi S and Fong P. (2020). Efficient Authorization of Graph-database Queries in an Attribute-supporting ReBAC Model. ACM Transactions on Privacy and Security. 23:4. (1-33). Online publication date: 30-Nov-2020. https://doi.org/10.1145/3401027 163. Komar K, Santra A, Bhowmick S and Chakravarthy S. EERMLN: EER Approach for Modeling, Mapping, and Analyzing Complex Data Using Multilayer Networks (MLNs). Conceptual Modeling. (555-572). https://doi.org/10.1007/978-3-030-62522-1_41 164. Wu H, Fu Z and Wang Y. (2020). A medical network clustering method with weighted graph structure. Measurement and Control. 10.1177/0020294020952469. 53:9-10. (1751-1759). Online publication date: 1-Nov-2020. http://journals.sagepub.com/doi/10.1177/0020294020952469 165. Chen L, Goranci G, Henzinger M, Peng R and Saranurak T. (2020). Fast Dynamic Cuts, Distances and Effective Resistances via Vertex Sparsifiers 2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS). 10.1109/FOCS46700.2020.00109. 978-1-7281-9621-3. (1135-1146). https://ieeexplore.ieee.org/document/9317991/ 166. Canbaz Y, Dogrusoz U, Celiksoy M, Gungor F and Kurban K. (2020). Hydra: detecting fraud in financial transactions via graph based representation and visual analysis 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). 10.1109/ISMSIT50672.2020.9255191. 978-1-7281-9090-7. (1-6). https://ieeexplore.ieee.org/document/9255191/ 167. Badica C and Mladin S. (2020). Conference Support System through Scientometry and Collaboration Analytics 2020 24th International Conference on System Theory, Control and Computing (ICSTCC). 10.1109/ICSTCC50638.2020.9259731. 978-1-7281-9809-5. (546-552). https://ieeexplore.ieee.org/document/9259731/ 168. Lambers L, Schneider S and Weisgut M. (2020). Model-Based Testing of Read Only Graph Queries 2020 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). 10.1109/ICSTW50294.2020.00022. 978-1-7281-1075-2. (24-34). https://ieeexplore.ieee.org/document/9155876/ 169. Zhang W, Shang R and Jiao L. (2020). Complex network graph embedding method based on shortest path and MOEA/D for community detection. Applied Soft Computing. 10.1016/j.asoc.2020.106764. (106764). Online publication date: 1-Oct-2020. https://linkinghub.elsevier.com/retrieve/pii/S156849462030702X 170. Frozza A and Mello R. (2020). JS4Geo: a canonical JSON Schema for geographic data suitable to NoSQL databases. Geoinformatica. 24:4. (987-1019). Online publication date: 1-Oct-2020. https://doi.org/10.1007/s10707-020-00415-w 171. Reina F, Huf A, Presser D and Siqueira F. Modeling and Enforcing Integrity Constraints on Graph Databases. Database and Expert Systems Applications. (269-284). https://doi.org/10.1007/978-3-030-59003-1_18 172. Krommyda M and Kantere V. (2020). Spatial Data Management in IoT systems: A study of available storage and indexing solutions 2020 Second International Conference on Transdisciplinary AI (TransAI). 10.1109/TransAI49837.2020.00033. 978-1-7281-8699-3. (146-153). https://ieeexplore.ieee.org/document/9253152/ 173. Mondal S, Basu A and Mukherjee N. (2020). Building a trust-based doctor recommendation system on top of multilayer graph database. Journal of Biomedical Informatics. 10.1016/j.jbi.2020.103549. (103549). Online publication date: 1-Aug-2020. https://linkinghub.elsevier.com/retrieve/pii/S1532046420301775 174. Arsintescu B, Deo S and Harris W. (2019). PathQuery Pregel: high-performance graph query with bulk synchronous processing. Pattern Analysis and Applications. 10.1007/s10044-019-00841-z. 23:3. (1493-1504). Online publication date: 1-Aug-2020. http://link.springer.com/10.1007/s10044-019-00841-z 175. Veerman J and Lyons R. (2020). A Primer on Laplacian Dynamics in Directed Graphs. Nonlinear Phenomena in Complex Systems. 10.33581/1561-4085-2020-23-2-196-206. 23:2. (196-206). Online publication date: 9-Jul-2020. http://www.j-npcs.org/abstracts/vol2020/v23no2/v23no2p196.html 176. Chen D, Jalilifard A, Veloso A and Ziviani N. (2020). Modeling Pharmacological Effects with Multi-Relation Unsupervised Graph Embedding 2020 International Joint Conference on Neural Networks (IJCNN). 10.1109/IJCNN48605.2020.9206668. 978-1-7281-6926-2. (1-7). https://ieeexplore.ieee.org/document/9206668/ 177. Arias J. (2020). The Benefits of Graph Databases for the Computation of Clinical Quality Measures 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS). 10.1109/CBMS49503.2020.00088. 978-1-7281-9429-5. (433-436). https://ieeexplore.ieee.org/document/9182798/ 178. Patibandla R. (2020). Regularization of Graphs. Recommender System with Machine Learning and Artificial Intelligence. 10.1002/9781119711582.ch19. (373-386). Online publication date: 17-Jun-2020. https://onlinelibrary.wiley.com/doi/10.1002/9781119711582.ch19 179. Sunkle S, Saxena K, Patil A, Kulkarni V, Jain D, Chacko R and Rai B. Information Extraction and Graph Representation for the Design of Formulated Products. Advanced Information Systems Engineering. (433-448). https://doi.org/10.1007/978-3-030-49435-3_27 180. Pivert O, Scholly E, Smits G and Thion V. (2020). Fuzzy quality-Aware queries to graph databases. Information Sciences: an International Journal. 521:C. (160-173). Online publication date: 1-Jun-2020. https://doi.org/10.1016/j.ins.2020.02.035 181. Azad P, Navimipour N, Rahmani A and Sharifi A. (2020). The role of structured and unstructured data managing mechanisms in the Internet of things. Cluster Computing. 23:2. (1185-1198). Online publication date: 1-Jun-2020. https://doi.org/10.1007/s10586-019-02986-2 182. Lu J and Holubová I. (2019). Multi-model Databases. ACM Computing Surveys. 52:3. (1-38). Online publication date: 31-May-2020. https://doi.org/10.1145/3323214 183. Gómez L, Kuijpers B and Vaisman A. Online analytical processsing on graph data. Intelligent Data Analysis. 10.3233/IDA-194576. 24:3. (515-541). https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/IDA-194576 184. Polyvyanyy A, Pika A and Hofstede A. (2020). Scenario-based process querying for compliance, reuse, and standardization. Information Systems. 10.1016/j.is.2020.101563. (101563). Online publication date: 1-May-2020. https://linkinghub.elsevier.com/retrieve/pii/S0306437920300569 185. Ahmed A, Hassan Z and Shabbir M. (2020). Interpretable Multi-Scale Graph Descriptors via Structural Compression. Information Sciences. 10.1016/j.ins.2020.05.032. Online publication date: 1-May-2020. https://linkinghub.elsevier.com/retrieve/pii/S0020025520304266 186. Jovanovic P, Nadal S, Romero O, Abelló A and Bilalli B. (2020). Quarry: A User-centered Big Data Integration Platform. Information Systems Frontiers. 10.1007/s10796-020-10001-y. http://link.springer.com/10.1007/s10796-020-10001-y 187. Lin P, Song Q, Wu Y and Pi J. (2020). Repairing Entities using Star Constraints in Multirelational Graphs 2020 IEEE 36th International Conference on Data Engineering (ICDE). 10.1109/ICDE48307.2020.00027. 978-1-7281-2903-7. (229-240). https://ieeexplore.ieee.org/document/9101352/ 188. Candell R, Kashef M, Liu Y, Montgomery K and Foufou S. (2020). A Graph Database Approach to Wireless IIoT Workcell Performance Evaluation 2020 IEEE International Conference on Industrial Technology (ICIT). 10.1109/ICIT45562.2020.9067199. 978-1-7281-5754-2. (251-258). https://ieeexplore.ieee.org/document/9067199/ 189. Shimomura L, Oyamada R, Vieira M and Kaster D. (2020). A survey on graph-based methods for similarity searches in metric spaces. Information Systems. 10.1016/j.is.2020.101507. (101507). Online publication date: 1-Feb-2020. https://linkinghub.elsevier.com/retrieve/pii/S0306437920300181 190. Polyakov I, Chepovskiy A and Chepovskiy A. (2020). Data Compression in Big Graph Warehouse. Journal of Mathematical Sciences. 10.1007/s10958-020-04686-4. http://link.springer.com/10.1007/s10958-020-04686-4 191. Bayitaa S, Nanor E, Kpiebaareh M, Agyemang B and Wu W. Graph Based Analytics Enhanced by Deep Learning. Proceedings of 2020 6th International Conference on Computing and Data Engineering. (39-43). https://doi.org/10.1145/3379247.3379286 192. Sunkle S, Jain D, Saxena K, Patil A, Chacko R and Rai B. (2020). Generate and Test for Formulated Product Variants With Information Extraction and an In-Silico Model. Advanced Digital Architectures for Model-Driven Adaptive Enterprises. 10.4018/978-1-7998-0108-5.ch010. (223-250). http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-7998-0108-5.ch010 193. Dhiman A and Toshniwal D. An Approximate Model for Event Detection From Twitter Data. IEEE Access. 10.1109/ACCESS.2020.3007004. 8. (122168-122184). https://ieeexplore.ieee.org/document/9133064/ 194. Chen F, Wang Y, Wang B and Kuo C. (2020). (2020). Graph representation learning: a survey. APSIPA Transactions on Signal and Information Processing. 10.1017/ATSIP.2020.13. 9:1. http://www.nowpublishers.com/article/Details/SIP-151 195. Guzzi P and Roy S. (2020). Biological network databases. Biological Network Analysis. 10.1016/B978-0-12-819350-1.00011-6. (77-93). https://linkinghub.elsevier.com/retrieve/pii/B9780128193501000116 196. Chen J, Song Q, Zhao C and Li Z. (2020). Graph Database and Relational Database Performance Comparison on a Transportation Network. Advances in Computing and Data Sciences. 10.1007/978-981-15-6634-9_37. (407-418). http://link.springer.com/10.1007/978-981-15-6634-9_37 197. Wang X and Chen W. (2020). Knowledge Graph Data Management: Models, Methods, and Systems. Web Information Systems Engineering. 10.1007/978-981-15-3281-8_1. (3-12). http://link.springer.com/10.1007/978-981-15-3281-8_1 198. Zhuge H. (2020). Cyber-Physical-Social Semantic Link Network. Cyber-Physical-Social Intelligence. 10.1007/978-981-13-7311-4_3. (55-141). http://link.springer.com/10.1007/978-981-13-7311-4_3 199. Khosrowjerdi H, Nemati H and Meinke K. (2020). Spatio-Temporal Model-Checking of Cyber-Physical Systems Using Graph Queries. Tests and Proofs. 10.1007/978-3-030-50995-8_4. (59-79). http://link.springer.com/10.1007/978-3-030-50995-8_4 200. Banning E. (2020). Compilations: Designing and Using Archaeological Databases. The Archaeologist's Laboratory. 10.1007/978-3-030-47992-3_4. (43-58). http://link.springer.com/10.1007/978-3-030-47992-3_4 201. Lukyanenko R and Parsons J. (2020). Easier Crowdsourcing Is Better: Designing Crowdsourcing Systems to Increase Information Quality and User Participation. Design Science Research. Cases. 10.1007/978-3-030-46781-4_3. (43-72). http://link.springer.com/10.1007/978-3-030-46781-4_3 202. Fensel D, Şimşek U, Angele K, Huaman E, Kärle E, Panasiuk O, Toma I, Umbrich J and Wahler A. (2020). How to Build a Knowledge Graph. Knowledge Graphs. 10.1007/978-3-030-37439-6_2. (11-68). http://link.springer.com/10.1007/978-3-030-37439-6_2 203. Fensel D, Şimşek U, Angele K, Huaman E, Kärle E, Panasiuk O, Toma I, Umbrich J and Wahler A. (2020). Introduction: What Is a Knowledge Graph?. Knowledge Graphs. 10.1007/978-3-030-37439-6_1. (1-10). http://link.springer.com/10.1007/978-3-030-37439-6_1 204. Sharma C and Sinha R. A Schema-First Formalism for Labeled Property Graph Databases. Proceedings of the 6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies. (71-80). https://doi.org/10.1145/3365109.3368782 205. Prabhu S, Murthy K and Subramanyam N. (2019). Validating Tantra Framework using entropy. Social Network Analysis and Mining. 10.1007/s13278-019-0562-1. 9:1. Online publication date: 1-Dec-2019. http://link.springer.com/10.1007/s13278-019-0562-1 206. Mennicke S. Modal Schema Graphs for Graph Databases. Conceptual Modeling. (498-512). https://doi.org/10.1007/978-3-030-33223-5_41 207. Steinmetz D, Merz F, Ma H and Hartmann S. A Graph Model for Taxi Ride Sharing Supported by Graph Databases. Conceptual Modeling. (108-116). https://doi.org/10.1007/978-3-030-33223-5_10 208. Sheng S, Zhou P and Wu X. (2019). CEPV: A Tree Structure Information Extraction and Visualization Tool for Big Knowledge Graph 2019 IEEE International Conference on Big Knowledge (ICBK). 10.1109/ICBK.2019.00037. 978-1-7281-4607-2. (221-228). https://ieeexplore.ieee.org/document/8944537/ 209. Qian Q, Wang Y and Zhao S. (2019). Materials data specification: Methods and use cases. Computational Materials Science. 10.1016/j.commatsci.2019.109086. 169. (109086). Online publication date: 1-Nov-2019. https://linkinghub.elsevier.com/retrieve/pii/S0927025619303775 210. Wirawan P, Er Riyanto D, Nugraheni D and Yasmin Y. (2019). Graph Database Schema for Multimodal Transportation in Semarang. Journal of Information Systems Engineering and Business Intelligence. 10.20473/jisebi.5.2.163-170. 5:2. (163). https://e-journal.unair.ac.id/JISEBI/article/view/12013 211. Meixner K, Winkler D, Wapp M, Rosendahl R and Biffl S. Investigating the Performance of selected Data Storage Concepts for AutomationML Models. IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society. (2785-2791). https://doi.org/10.1109/IECON.2019.8927275 212. Gómez L, Kuijpers B and Vaisman A. (2019). Analytical queries on semantic trajectories using graph databases. Transactions in GIS. 10.1111/tgis.12556. 23:5. (1078-1101). Online publication date: 1-Oct-2019. https://onlinelibrary.wiley.com/doi/10.1111/tgis.12556 213. Wu J and Nakamoto Y. (2019). RelSeeker: Relationship-based Query Language in a Graph Database for Social Networks 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS). 10.1109/SNAMS.2019.8931872. 978-1-7281-2946-4. (268-273). https://ieeexplore.ieee.org/document/8931872/ 214. Barakat O, Ventre P, Salsano S and Fu X. (2019). Busoni: Policy Composition and Northbound Interface for IPv6 Segment Routing Networks 2019 IEEE 27th International Conference on Network Protocols (ICNP). 10.1109/ICNP.2019.8888104. 978-1-7281-2700-2. (1-4). https://ieeexplore.ieee.org/document/8888104/ 215. Veerman J and Kummel E. (2019). Diffusion and consensus on weakly connected directed graphs. Linear Algebra and its Applications. 10.1016/j.laa.2019.05.014. 578. (184-206). Online publication date: 1-Oct-2019. https://linkinghub.elsevier.com/retrieve/pii/S002437951930223X 216. Surinx D, Van den Bussche J and Van Gucht D. (2019). A framework for comparing query languages in their ability to express boolean queries. Annals of Mathematics and Artificial Intelligence. 87:1-2. (157-184). Online publication date: 1-Oct-2019. https://doi.org/10.1007/s10472-019-09639-5 217. Reyes A, Hernandez A, Gutierrez R, Bolivar N, Jimenez D, Bastidas J and Solano J. (2019). A Novel Extended Graph Strategy to Model Microgrids 2019 International Conference on Smart Energy Systems and Technologies (SEST). 10.1109/SEST.2019.8849117. 978-1-7281-1156-8. (1-6). https://ieeexplore.ieee.org/document/8849117/ 218. Anikin D, Borisenko O and Nedumov Y. (2019). Labeled Property Graphs: SQL or NoSQL? 2019 Ivannikov Memorial Workshop (IVMEM). 10.1109/IVMEM.2019.00007. 978-1-7281-4623-2. (7-13). https://ieeexplore.ieee.org/document/8880730/ 219. Álvarez-García S, Freire B, Ladra S and Pedreira Ó. (2022). Compact and efficient representation of general graph databases. Knowledge and Information Systems. 60:3. (1479-1510). Online publication date: 1-Sep-2019. https://doi.org/10.1007/s10115-018-1275-x 220. Wang Q, He S, Zheng X and Zeng D. (2019). Marketing Pattern Risks Detection Based on Semi-Supervised Learning 2019 IEEE International Conference on Intelligence and Security Informatics (ISI). 10.1109/ISI.2019.8823291. 978-1-7281-2504-6. (229-229). https://ieeexplore.ieee.org/document/8823291/ 221. Sharma C, Sinha R and Leitao P. (2019). IASelect: Finding Best-fit Agent Practices in Industrial CPS Using Graph Databases 2019 IEEE 17th International Conference on Industrial Informatics (INDIN). 10.1109/INDIN41052.2019.8972272. 978-1-7281-2927-3. (1558-1563). https://ieeexplore.ieee.org/document/8972272/ 222. Yerashenia N and Bolotov A. (2019). Computational Modelling for Bankruptcy Prediction: Semantic Data Analysis Integrating Graph Database and Financial Ontology 2019 IEEE 21st Conference on Business Informatics (CBI). 10.1109/CBI.2019.00017. 978-1-7281-0650-2. (84-93). https://ieeexplore.ieee.org/document/8808039/ 223. Pivert O, Slama O and Thion V. (2019). Expression and efficient evaluation of fuzzy quantified structural queries to fuzzy graph databases. Fuzzy Sets and Systems. 366:C. (3-17). Online publication date: 1-Jul-2019. https://doi.org/10.1016/j.fss.2018.06.002 224. Petnga L. (2019). Graph‐based Assessment and Analysis of System Architecture Models. INCOSE International Symposium. 10.1002/j.2334-5837.2019.00644.x. 29:1. (922-936). Online publication date: 1-Jul-2019. https://incose.onlinelibrary.wiley.com/doi/10.1002/j.2334-5837.2019.00644.x 225. Chaudhry H. FlowGraph. Proceedings of the 13th ACM International Conference on Distributed and Event-based Systems. (272-275). https://doi.org/10.1145/3328905.3332303 226. Tahboub R, Wu X, Essertel G and Rompf T. Towards compiling graph queries in relational engines. Proceedings of the 17th ACM SIGPLAN International Symposium on Database Programming Languages. (30-41). https://doi.org/10.1145/3315507.3330200 227. Costa B and Cura L. An Neo4j implementation for designing fuzzy graph databases. Proceedings of the 23rd International Database Applications & Engineering Symposium. (1-6). https://doi.org/10.1145/3331076.3331082 228. Ma H, Alipourlangouri M, Wu Y, Chiang F and Pi J. (2019). Ontology-based entity matching in attributed graphs. Proceedings of the VLDB Endowment. 12:10. (1195-1207). Online publication date: 1-Jun-2019. https://doi.org/10.14778/3339490.3339501 229. Barakat O, Koll D and Fu X. Gavel: A Fast and Easy-to-Use Plain Data Representation for Software-Defined Networks. IEEE Transactions on Network and Service Management. 10.1109/TNSM.2019.2903440. 16:2. (606-617). https://ieeexplore.ieee.org/document/8661512/ 230. Hajeer M and Dasgupta D. Handling Big Data Using a Data-Aware HDFS and Evolutionary Clustering Technique. IEEE Transactions on Big Data. 10.1109/TBDATA.2017.2782785. 5:2. (134-147). https://ieeexplore.ieee.org/document/8197362/ 231. Heidari S, Simmhan Y, Calheiros R and Buyya R. (2018). Scalable Graph Processing Frameworks. ACM Computing Surveys. 51:3. (1-53). Online publication date: 31-May-2019. https://doi.org/10.1145/3199523 232. Moore J, Boland M, Camara P, Chervitz H, Gonzalez G, Himes B, Kim D, Mowery D, Ritchie M, Shen L, Urbanowicz R and Holmes J. (2019). Preparing next-generation scientists for biomedical big data: artificial intelligence approaches. Personalized Medicine. 10.2217/pme-2018-0145. 16:3. (247-257). Online publication date: 1-May-2019. https://www.tandfonline.com/doi/full/10.2217/pme-2018-0145 233. Sinha R, Dowdeswell B, Zhabelova G and Vyatkin V. (2018). TORUS. ACM Transactions on Cyber-Physical Systems. 3:2. (1-25). Online publication date: 30-Apr-2019. https://doi.org/10.1145/3203208 234. Kattepur A and P B. RoboPlanner. Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing. (953-956). https://doi.org/10.1145/3297280.3297568 235. Quaas K and Shirmohammadi M. (2019). Synchronizing Data Words for Register Automata. ACM Transactions on Computational Logic. 20:2. (1-27). Online publication date: 4-Apr-2019. https://doi.org/10.1145/3309760 236. Davoudian A, Chen L and Liu M. (2018). A Survey on NoSQL Stores. ACM Computing Surveys. 51:2. (1-43). Online publication date: 31-Mar-2019. https://doi.org/10.1145/3158661 237. Lukyanenko R, Parsons J and Samuel B. (2018). Representing instances: the case for reengineering conceptual modelling grammars. European Journal of Information Systems. 10.1080/0960085X.2018.1488567. 28:1. (68-90). Online publication date: 2-Jan-2019. https://www.tandfonline.com/doi/full/10.1080/0960085X.2018.1488567 238. Van Erven G, Carvalho R, Cordeiro da Silva W, Lifschitz S, Vera-Olivera H and Holanda M. Designing Graph Databases With GRAPHED. Journal of Database Management. 10.4018/JDM.2019010103. 30:1. (41-60). http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/JDM.2019010103 239. Lukyanenko R and Parsons J. (2019). Beyond Micro-Tasks. Crowdsourcing. 10.4018/978-1-5225-8362-2.ch076. (1510-1535). http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-8362-2.ch076 240. Lukyanenko R and Parsons J. (2019). Beyond Micro-Tasks. Social Entrepreneurship. 10.4018/978-1-5225-8182-6.ch072. (1403-1428). http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-8182-6.ch072 241. Guerreiro S. Challenges of Meta Access Control Model Enforcement to an Increased Interoperability. Advanced Methodologies and Technologies in Business Operations and Management. 10.4018/978-1-5225-7362-3.ch018. (247-258). http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-7362-3.ch018 242. Kejriwal M, Lopez V, Sequeda J, Fionda V, Pirrò G, Consens M, Kejriwal M, Lopez V and Sequeda J. (2019). Querying knowledge graphs with extended property paths. Semantic Web. 10:6. (1127-1168). Online publication date: 1-Jan-2019. https://doi.org/10.3233/SW-190365 243. Yuan Y, Soh D, Yang H and Quek T. Learning Overlapping Community-based Networks. IEEE Transactions on Signal and Information Processing over Networks. 10.1109/TSIPN.2019.2936361. (1-1). https://ieeexplore.ieee.org/document/8807284/ 244. Reddy K. Interactive Graph Data Integration System With Spatial-Oriented Visualization and Feedback-Driven Provenance. IEEE Access. 10.1109/ACCESS.2019.2928847. 7. (101336-101344). https://ieeexplore.ieee.org/document/8763970/ 245. Tushkanova O and Samoylov V. (2019). Knowledge Net: Model and System for Accumulation, Representation, and Use of Knowledge. IFAC-PapersOnLine. 10.1016/j.ifacol.2019.11.351. 52:13. (1150-1155). https://linkinghub.elsevier.com/retrieve/pii/S2405896319313291 246. Paulin A. (2019). Choosing Fitting Technology. Smart City Governance. 10.1016/B978-0-12-816224-8.00012-1. (219-236). https://linkinghub.elsevier.com/retrieve/pii/B9780128162248000121 247. Dhillon S. (2019). Biological Databases. Encyclopedia of Bioinformatics and Computational Biology. 10.1016/B978-0-12-809633-8.20198-2. (96-117). https://linkinghub.elsevier.com/retrieve/pii/B9780128096338201982 248. Bolla S, Tirumalasetty S and Jyothi S. (2019). Anatomization of Document-Based NoSQL Databases. Innovations in Computer Science and Engineering. 10.1007/978-981-13-7082-3_65. (569-577). http://link.springer.com/10.1007/978-981-13-7082-3_65 249. Meier A and Kaufmann M. (2019). NoSQL Databases. SQL & NoSQL Databases. 10.1007/978-3-658-24549-8_7. (201-218). http://link.springer.com/10.1007/978-3-658-24549-8_7 250. Paradies M, Plantikow S and Rest O. (2019). Graph Data Management Systems. Encyclopedia of Big Data Technologies. 10.1007/978-3-319-77525-8_82. (822-830). http://link.springer.com/10.1007/978-3-319-77525-8_82 251. Gutiérrez C, Hidders J and Wood P. (2019). Graph Data Models. Encyclopedia of Big Data Technologies. 10.1007/978-3-319-77525-8_81. (830-835). http://link.springer.com/10.1007/978-3-319-77525-8_81 252. Bhattacharyya A, Baldin I, Xin Y and Anyanwu K. (2019). Evaluating Generalized Path Queries by Integrating Algebraic Path Problem Solving with Graph Pattern Matching. Semantic Systems. The Power of AI and Knowledge Graphs. 10.1007/978-3-030-33220-4_8. (101-116). http://link.springer.com/10.1007/978-3-030-33220-4_8 253. Qassimi S and Abdelwahed E. (2019). Semantic Graph-Based Recommender System. Application in Cultural Heritage. New Trends in Model and Data Engineering. 10.1007/978-3-030-32213-7_8. (109-121). http://link.springer.com/10.1007/978-3-030-32213-7_8 254. Comyn-Wattiau I and Akoka J. (2019). Query-Based Reverse Engineering of Graph Databases – From Program to Model. New Trends in Databases and Information Systems. 10.1007/978-3-030-30278-8_22. (188-197). http://link.springer.com/10.1007/978-3-030-30278-8_22 255. Vaisman A, Besteiro F and Valverde M. (2019). Modelling and Querying Star and Snowflake Warehouses Using Graph Databases. New Trends in Databases and Information Systems. 10.1007/978-3-030-30278-8_18. (144-152). https://link.springer.com/10.1007/978-3-030-30278-8_18 256. Schneider S, Lambers L and Orejas F. (2019). A Logic-Based Incremental Approach to Graph Repair. Fundamental Approaches to Software Engineering. 10.1007/978-3-030-16722-6_9. (151-167). http://link.springer.com/10.1007/978-3-030-16722-6_9 257. Akintoye S, Bagula A, Isafiade O, Djemaiel Y and Boudriga N. (2019). Data Model for Cloud Computing Environment. e-Infrastructure and e-Services for Developing Countries. 10.1007/978-3-030-16042-5_19. (199-215). http://link.springer.com/10.1007/978-3-030-16042-5_19 258. Ramler R, Buchgeher G, Klammer C, Pfeiffer M, Salomon C, Thaller H and Linsbauer L. (2019). Benefits and Drawbacks of Representing and Analyzing Source Code and Software Engineering Artifacts with Graph Databases. Software Quality: The Complexity and Challenges of Software Engineering and Software Quality in the Cloud. 10.1007/978-3-030-05767-1_9. (125-148). http://link.springer.com/10.1007/978-3-030-05767-1_9 259. JASKIERNY L. (2018). REVIEW OF THE DATA MODELING STANDARDS AND DATA MODEL TRANSFORMATION TECHNIQUES. Applied Computer Science. 10.35784/acs-2018-32. 14:4. (93-108). https://ph.pollub.pl/index.php/acs/article/view/3268 260. Lissandrini M, Brugnara M and Velegrakis Y. (2018). Beyond macrobenchmarks. Proceedings of the VLDB Endowment. 12:4. (390-403). Online publication date: 1-Dec-2018. https://doi.org/10.14778/3297753.3297759 261. Musa A, Dehmer M, Yli-Harja O and Emmert-Streib F. (2018). Exploiting Genomic Relations in Big Data Repositories by Graph-Based Search Methods. Machine Learning and Knowledge Extraction. 10.3390/make1010012. 1:1. (205-210). http://www.mdpi.com/2504-4990/1/1/12 262. Fortini P and Davis C. Analysis, Integration and Visualization of Urban Data From Multiple Heterogeneous Sources. Proceedings of the 1st ACM SIGSPATIAL Workshop on Advances on Resilient and Intelligent Cities. (17-26). https://doi.org/10.1145/3284566.3284569 263. Schneider S, Lambers L and Orejas F. (2018). Automated reasoning for attributed graph properties. International Journal on Software Tools for Technology Transfer (STTT). 20:6. (705-737). Online publication date: 1-Nov-2018. /doi/10.5555/3288063.3288080 264. Yang C, Feng Y, Li P, Shi Y and Han J. (2018). Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights 2018 IEEE International Conference on Data Mining (ICDM). 10.1109/ICDM.2018.00081. 978-1-5386-9159-5. (657-666). https://ieeexplore.ieee.org/document/8594890/ 265. Schneider S, Lambers L and Orejas F. (2018). Automated reasoning for attributed graph properties. International Journal on Software Tools for Technology Transfer. 10.1007/s10009-018-0496-3. 20:6. (705-737). Online publication date: 1-Nov-2018. http://link.springer.com/10.1007/s10009-018-0496-3 266. Bonifati A, Fletcher G, Voigt H and Yakovets N. (2018). Querying Graphs. Synthesis Lectures on Data Management. 10.2200/S00873ED1V01Y201808DTM051. 10:3. (1-184). Online publication date: 1-Oct-2018. https://www.morganclaypool.com/doi/10.2200/S00873ED1V01Y201808DTM051 267. Ayed R. (2018). Social Networks Analysis in a Business Intelligence Context 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA). 10.1109/AICCSA.2018.8612832. 978-1-5386-9120-5. (1-8). https://ieeexplore.ieee.org/document/8612832/ 268. Castelltort A and Martin T. (2018). Handling scalable approximate queries over NoSQL graph databases: Cypherf and the Fuzzy4S framework. Fuzzy Sets and Systems. 10.1016/j.fss.2017.08.002. 348. (21-49). Online publication date: 1-Oct-2018. https://linkinghub.elsevier.com/retrieve/pii/S0165011417303093 269. Mattern H and König M. (2018). BIM-based modeling and management of design options at early planning phases. Advanced Engineering Informatics. 38:C. (316-329). Online publication date: 1-Oct-2018. https://doi.org/10.1016/j.aei.2018.08.007 270. Angles R, Arenas M, Barceló P, Hogan A, Reutter J and Vrgoč D. (2017). Foundations of Modern Query Languages for Graph Databases. ACM Computing Surveys. 50:5. (1-40). Online publication date: 30-Sep-2018. https://doi.org/10.1145/3104031 271. Castelltort A, Laurent A, Pivert O, Slama O and Thion V. (2018). Fuzzy Preference Queries to NoSQL Graph Databases. NoSQL Data Models. 10.1002/9781119528227.ch6. (167-201). Online publication date: 15-Aug-2018. https://onlinelibrary.wiley.com/doi/10.1002/9781119528227.ch6 272. Głuch G, Marcinkowski J and Ostropolski-Nalewaja P. Can One Escape Red Chains?. Proceedings of the 33rd Annual ACM/IEEE Symposium on Logic in Computer Science. (492-501). https://doi.org/10.1145/3209108.3209120 273. Morgado C, Busichia Baioco G, Basso T and Moraes R. (2018). A Security Model for Access Control in Graph-Oriented Databases 2018 IEEE International Conference on Software Quality, Reliability and Security (QRS). 10.1109/QRS.2018.00027. 978-1-5386-7757-5. (135-142). https://ieeexplore.ieee.org/document/8424965/ 274. Verginadis Y, Patiniotakis I and Mentzas G. (2018). Metadata Schema for Data-Aware Multi-Cloud Computing 2018 Innovations in Intelligent Systems and Applications (INISTA). 10.1109/INISTA.2018.8466270. 978-1-5386-5150-6. (1-9). https://ieeexplore.ieee.org/document/8466270/ 275. Mezzanzanica M, Mercorio F, Cesarini M, Moscato V and Picariello A. (2018). GraphDBLP. Multimedia Tools and Applications. 77:14. (18657-18688). Online publication date: 1-Jul-2018. https://doi.org/10.1007/s11042-017-5503-2 276. Mayer C, Mayer R, Grunert J, Rothermel K and Tariq M. Q-graph. Proceedings of the 1st ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA). (1-10). https://doi.org/10.1145/3210259.3210265 277. Biscarini F, Cozzi P and Orozco‐ter Wengel P. (2018). Lessons learnt on the analysis of large sequence data in animal genomics. Animal Genetics. 10.1111/age.12655. 49:3. (147-158). Online publication date: 1-Jun-2018. https://onlinelibrary.wiley.com/doi/10.1111/age.12655 278. Liu M, Shang W, Cao J, Pan C, Lin W and Fu H. (2018). The Design and Implementation of Script Authoring Assistant System of Film and Television Big Data 2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS). 10.1109/ICIS.2018.8466381. 978-1-5386-5892-5. (579-584). https://ieeexplore.ieee.org/document/8466381/ 279. Bolla S and Sudhir T. (2018). Pinning Optimal DBDBs for User Delivered Sentiments 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS). 10.1109/ICCONS.2018.8662989. 978-1-5386-2842-3. (899-904). https://ieeexplore.ieee.org/document/8662989/ 280. Francis N, Green A, Guagliardo P, Libkin L, Lindaaker T, Marsault V, Plantikow S, Rydberg M, Selmer P and Taylor A. Cypher. Proceedings of the 2018 International Conference on Management of Data. (1433-1445). https://doi.org/10.1145/3183713.3190657 281. Angles R, Arenas M, Barcelo P, Boncz P, Fletcher G, Gutierrez C, Lindaaker T, Paradies M, Plantikow S, Sequeda J, van Rest O and Voigt H. G-CORE. Proceedings of the 2018 International Conference on Management of Data. (1421-1432). https://doi.org/10.1145/3183713.3190654 282. Jamkhedkar P, Johnson T, Kanza Y, Shaikh A, Shankaranarayanan N and Shkapenyuk V. A Graph Database for a Virtualized Network Infrastructure. Proceedings of the 2018 International Conference on Management of Data. (1393-1405). https://doi.org/10.1145/3183713.3190653 283. Zou J, Lang B, Zhao J and Zhao Y. Achieving effective and efficient attributed graph data management using lucene. Proceedings of the 1st International Conference on Big Data Technologies. (7-13). https://doi.org/10.1145/3226116.3226119 284. Colaso A, Prieto P, Herrero J, Abad P, Menezo L, Puente V and Gregorio J. Memory Hierarchy Characterization of NoSQL Applications through Full-System Simulation. IEEE Transactions on Parallel and Distributed Systems. 10.1109/TPDS.2017.2787150. 29:5. (1161-1173). https://ieeexplore.ieee.org/document/8239962/ 285. Kovacs L, Varga E and Balla T. (2018). Efficiency analysis of ontology servers 2018 19th International Carpathian Control Conference (ICCC). 10.1109/CarpathianCC.2018.8399655. 978-1-5386-4762-2. (353-358). https://ieeexplore.ieee.org/document/8399655/ 286. Hogan A. Profiling Graphs. Companion Proceedings of the The Web Conference 2018. (1481-1482). https://doi.org/10.1145/3184558.3191603 287. Libkin L, Reutter J, Soto A and Vrgoč D. (2018). TriAL. ACM Transactions on Database Systems. 43:1. (1-46). Online publication date: 31-Mar-2018. https://doi.org/10.1145/3154385 288. Rizvi S and Fong P. Efficient Authorization of Graph Database Queries in an Attribute-Supporting ReBAC Model. Proceedings of the Eighth ACM Conference on Data and Application Security and Privacy. (204-211). https://doi.org/10.1145/3176258.3176331 289. Kostylev E, Reutter J and Vrgoč D. (2018). Containment of queries for graphs with data. Journal of Computer and System Sciences. 10.1016/j.jcss.2017.09.005. 92. (65-91). Online publication date: 1-Mar-2018. https://linkinghub.elsevier.com/retrieve/pii/S002200001730137X 290. Küçükkeçeci C and Yazıcı A. (2018). Big Data Model Simulation on a Graph Database for Surveillance in Wireless Multimedia Sensor Networks. Big Data Research. 10.1016/j.bdr.2017.09.003. 11. (33-43). Online publication date: 1-Mar-2018. https://linkinghub.elsevier.com/retrieve/pii/S2214579617300102 291. Shen Y, Yuan K, Chen D, Colloc J, Yang M, Li Y and Lei K. (2018). An ontology-driven clinical decision support system (IDDAP) for infectious disease diagnosis and antibiotic prescription. Artificial Intelligence in Medicine. 86:C. (20-32). Online publication date: 1-Mar-2018. https://doi.org/10.1016/j.artmed.2018.01.003 292. Ramezanian S, Meskanen T and Niemi V. (2018). Privacy Preserving Queries on Directed Graph 2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS). 10.1109/NTMS.2018.8328714. 978-1-5386-3662-6. (1-5). http://ieeexplore.ieee.org/document/8328714/ 293. Parsons J and Lukyanenko R. (2018). Beyond Micro-Tasks. Journal of Database Management. 29:1. (1-22). Online publication date: 1-Jan-2018. https://doi.org/10.4018/JDM.2018010101 294. Ma X, Beaulieu S, Fu L, Fox P, Di Stefano M and West P. Documenting Provenance for Reproducible Marine Ecosystem Assessment in Open Science. Information Retrieval and Management. 10.4018/978-1-5225-5191-1.ch045. (1051-1077). http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-5191-1.ch045 295. Guerreiro S. Challenges of Meta Access Control Model Enforcement to an Increased Interoperability. Encyclopedia of Information Science and Technology, Fourth Edition. 10.4018/978-1-5225-2255-3.ch056. (651-661). http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-2255-3.ch056 296. YANG S, WANG J, SHI L, TAN Y and QIAO F. (2018). Engineering management for high-end equipment intelligent manufacturing. Frontiers of Engineering Management. 10.15302/J-FEM-2018050. 5:4. (420). http://engineering.cae.cn/fem/EN/10.15302/J-FEM-2018050 297. Pienta R, Hohman F, Endert A, Tamersoy A, Roundy K, Gates C, Navathe S and Chau D. VIGOR: Interactive Visual Exploration of Graph Query Results. IEEE Transactions on Visualization and Computer Graphics. 10.1109/TVCG.2017.2744898. 24:1. (215-225). http://ieeexplore.ieee.org/document/8019832/ 298. Poulovassilis A. (2018). Applications of Flexible Querying to Graph Data. Graph Data Management. 10.1007/978-3-319-96193-4_4. (97-142). http://link.springer.com/10.1007/978-3-319-96193-4_4 299. Angles R and Gutierrez C. (2018). An Introduction to Graph Data Management. Graph Data Management. 10.1007/978-3-319-96193-4_1. (1-32). http://link.springer.com/10.1007/978-3-319-96193-4_1 300. Villa F, Moreno F and Guzmán J. (2018). An Analysis of a Methodology that Transforms the Entity-Relationship Model into a Conceptual Model for a Graph Database. Emerging Technologies in Computing. 10.1007/978-3-319-95450-9_6. (70-83). http://link.springer.com/10.1007/978-3-319-95450-9_6 301. Surinx D, Van den Bussche J and Van Gucht D. (2018). A Framework for Comparing Query Languages in Their Ability to Express Boolean Queries. Foundations of Information and Knowledge Systems. 10.1007/978-3-319-90050-6_20. (360-378). http://link.springer.com/10.1007/978-3-319-90050-6_20 302. Van Erven G, Silva W, Carvalho R and Holanda M. (2018). GRAPHED: A Graph Description Diagram for Graph Databases. Trends and Advances in Information Systems and Technologies. 10.1007/978-3-319-77703-0_111. (1141-1151). http://link.springer.com/10.1007/978-3-319-77703-0_111 303. Sędziwy A and Kotulski L. (2018). Using a Multi-agent System for Overcoming Flickering Effect in Distributed Large-Scale Customized Lighting Design. Intelligent Information and Database Systems. 10.1007/978-3-319-75417-8_35. (372-381). https://link.springer.com/10.1007/978-3-319-75417-8_35 304. Paradies M, Plantikow S and Rest O. (2018). Graph Data Management Systems. Encyclopedia of Big Data Technologies. 10.1007/978-3-319-63962-8_82-1. (1-9). http://link.springer.com/10.1007/978-3-319-63962-8_82-1 305. Gutierrez C, Hidders J and Wood P. (2018). Graph Data Models. Encyclopedia of Big Data Technologies. 10.1007/978-3-319-63962-8_81-1. (1-6). http://link.springer.com/10.1007/978-3-319-63962-8_81-1 306. Asaad C and Baïna K. (2018). NoSQL Databases – Seek for a Design Methodology. Model and Data Engineering. 10.1007/978-3-030-00856-7_2. (25-40). http://link.springer.com/10.1007/978-3-030-00856-7_2 307. San Martín M and Gutierrez C. (2018). Transforming Social Networks Data. Encyclopedia of Social Network Analysis and Mining. 10.1007/978-1-4939-7131-2_389. (3170-3182). http://link.springer.com/10.1007/978-1-4939-7131-2_389 308. Cerinšek M and Batagelj V. (2018). Sources of Network Data. Encyclopedia of Social Network Analysis and Mining. 10.1007/978-1-4939-7131-2_313. (2843-2851). http://link.springer.com/10.1007/978-1-4939-7131-2_313 309. Wood P. (2018). Graph Database. Encyclopedia of Database Systems. 10.1007/978-1-4614-8265-9_183. (1639-1643). http://link.springer.com/10.1007/978-1-4614-8265-9_183 310. Gupta A. (2018). Graph Data Management in Scientific Applications. Encyclopedia of Database Systems. 10.1007/978-1-4614-8265-9_1298. (1636-1639). http://link.springer.com/10.1007/978-1-4614-8265-9_1298 311. Soga K, Casey G, Kumar K and Zhao B. (2017). Briefing: High-performance computing for city-scale modelling and simulations. Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction. 10.1680/jsmic.17.00026. 170:4. (80-85). Online publication date: 1-Dec-2017. https://www.icevirtuallibrary.com/doi/10.1680/jsmic.17.00026 312. Liu S, Lin G, Chai B, Dai J, Zhou A, Chen F and Pan J. (2017). The future of graph database applications: An electric utility perspective 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). 10.1109/ITNEC.2017.8284941. 978-1-5090-6414-4. (223-227). http://ieeexplore.ieee.org/document/8284941/ 313. Chai B, Qiu H, Liu S, Zhang B, Gao K, Zhou A and Chen F. (2017). A study of topology analysis engine based on graph databases in electric utilities 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). 10.1109/ITNEC.2017.8284800. 978-1-5090-6414-4. (585-588). http://ieeexplore.ieee.org/document/8284800/ 314. Almeida R, da Silva W, Castro K, Walter M, Araujo A, Holanda M and Lifschitz S. (2017). AProvBio: An architecture for data provenance in bioinformatics workflows using graph database 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 10.1109/BIBM.2017.8217989. 978-1-5090-3050-7. (2139-2144). http://ieeexplore.ieee.org/document/8217989/ 315. Zhang Y, Chen R and Chen H. Sub-millisecond Stateful Stream Querying over Fast-evolving Linked Data. Proceedings of the 26th Symposium on Operating Systems Principles. (614-630). https://doi.org/10.1145/3132747.3132777 316. Santisteban J and Ticona-Herrera R. (2017). Modeling a Persistent Graph 2017 Sixteenth Mexican International Conference on Artificial Intelligence (MICAI). 10.1109/MICAI-2017.2017.00011. 978-1-5386-7199-3. (15-22). https://ieeexplore.ieee.org/document/8575560/ 317. Tang P, Pitera J, Zubarev D and Chawla N. (2017). Materials Science Literature-Patent Relevance Search: A Heterogeneous Network Analysis Approach 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA). 10.1109/DSAA.2017.8. 978-1-5090-5004-8. (146-154). http://ieeexplore.ieee.org/document/8259773/ 318. Gutfraind A and Genkin M. (2017). A graph database framework for covert network analysis: An application to the Islamic State network in Europe. Social Networks. 10.1016/j.socnet.2016.10.004. 51. (178-188). Online publication date: 1-Oct-2017. https://linkinghub.elsevier.com/retrieve/pii/S0378873316302428 319. Zhu Y, Yan E and Song I. (2017). A natural language interface to a graph-based bibliographic information retrieval system. Data & Knowledge Engineering. 10.1016/j.datak.2017.06.006. 111. (73-89). Online publication date: 1-Sep-2017. https://linkinghub.elsevier.com/retrieve/pii/S0169023X17302823 320. Gómez L, Kuijpers B and Vaisman A. Performing OLAP over Graph Data. Proceedings of the International Workshop on Real-Time Business Intelligence and Analytics. (1-8). https://doi.org/10.1145/3129292.3129293 321. Aleithe M, Skowron P, Franczyk B and Sommer B. Data modeling of smart urban object networks. Proceedings of the International Conference on Web Intelligence. (1104-1109). https://doi.org/10.1145/3106426.3117759 322. Krleža D and Fertalj K. (2017). Graph Matching Using Hierarchical Fuzzy Graph Neural Networks. IEEE Transactions on Fuzzy Systems. 25:4. (892-904). Online publication date: 1-Aug-2017. https://doi.org/10.1109/TFUZZ.2016.2586962 323. Hall R, Murray C and Verdonk M. (2017). The Fragment Network: A Chemistry Recommendation Engine Built Using a Graph Database. Journal of Medicinal Chemistry. 10.1021/acs.jmedchem.7b00809. 60:14. (6440-6450). Online publication date: 27-Jul-2017. https://pubs.acs.org/doi/10.1021/acs.jmedchem.7b00809 324. Rigaux P and Thion V. Quality Awareness over Graph Pattern Queries. Proceedings of the 21st International Database Engineering & Applications Symposium. (90-97). https://doi.org/10.1145/3105831.3105871 325. D'Onofrio S, Wehrle M, Portmann E and Myrach T. Striving for semantic convergence with fuzzy cognitive maps and graph databases. 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). (1-6). https://doi.org/10.1109/FUZZ-IEEE.2017.8015657 326. Baier J, Daroch D, Reutter J and Vrgoč D. Evaluating Navigational RDF Queries over the Web. Proceedings of the 28th ACM Conference on Hypertext and Social Media. (165-174). https://doi.org/10.1145/3078714.3078731 327. Schulz H, Nocke T, Heitzler M and Schumann H. (2016). A systematic view on data descriptors for the visual analysis of tabular data. Information Visualization. 10.1177/1473871616667767. 16:3. (232-256). Online publication date: 1-Jul-2017. http://journals.sagepub.com/doi/10.1177/1473871616667767 328. Ravikumar G and Khaparde S. A Common Information Model Oriented Graph Database Framework for Power Systems. IEEE Transactions on Power Systems. 10.1109/TPWRS.2016.2631242. 32:4. (2560-2569). http://ieeexplore.ieee.org/document/7752988/ 329. Lin X, Peng Y, Choi B and Xu J. (2017). Human-Powered Data Cleaning for Probabilistic Reachability Queries on Uncertain Graphs. IEEE Transactions on Knowledge and Data Engineering. 29:7. (1452-1465). Online publication date: 1-Jul-2017. https://doi.org/10.1109/TKDE.2017.2684166 330. Pasquier T, Singh J, Eyers D and Bacon J. Camflow: Managed Data-Sharing for Cloud Services. IEEE Transactions on Cloud Computing. 10.1109/TCC.2015.2489211. 5:3. (472-484). http://ieeexplore.ieee.org/document/7295590/ 331. Paradies M and Voigt H. (2017). Big Graph Data Analytics on Single Machines – An Overview. Datenbank-Spektrum. 10.1007/s13222-017-0255-8. 17:2. (101-112). Online publication date: 1-Jul-2017. http://link.springer.com/10.1007/s13222-017-0255-8 332. Meyer H, Schering A and Heuer A. (2017). The Hydra.PowerGraph System. Datenbank-Spektrum. 10.1007/s13222-017-0253-x. 17:2. (113-129). Online publication date: 1-Jul-2017. http://link.springer.com/10.1007/s13222-017-0253-x 333. Reutter J, Romero M and Vardi M. (2017). Regular Queries on Graph Databases. Theory of Computing Systems. 61:1. (31-83). Online publication date: 1-Jul-2017. https://doi.org/10.1007/s00224-016-9676-2 334. De Virgilio R. Smart RDF Data storage in Graph Databases. Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. (872-881). https://doi.org/10.1109/CCGRID.2017.108 335. Jamkhedkar P, Johnson T, Kanza Y, Shaikh A, Shankarnarayanan N, Shkapenyuk V and Woodhull G. Virtualized Network Service Topology Exploration Using Nepal. Proceedings of the 2017 ACM International Conference on Management of Data. (1611-1614). https://doi.org/10.1145/3035918.3058733 336. Xirogiannopoulos K and Deshpande A. Extracting and Analyzing Hidden Graphs from Relational Databases. Proceedings of the 2017 ACM International Conference on Management of Data. (897-912). https://doi.org/10.1145/3035918.3035949 337. Francis N and Libkin L. Schema Mappings for Data Graphs. Proceedings of the 36th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems. (389-401). https://doi.org/10.1145/3034786.3056113 338. Akintoye S, Bagula A, Djemaiel Y and Boudriga N. (2017). Lightweight cloud computing for development: A graph based data model 2017 IST-Africa Week Conference (IST-Africa). 10.23919/ISTAFRICA.2017.8102365. . (1-14). http://ieeexplore.ieee.org/document/8102365/ 339. Tripathi G, Sharma B and Rajvanshi S. (2017). A combination of Internet of Things (IoT) and graph database for future battlefield systems 2017 International Conference on Computing, Communication and Automation (ICCCA). 10.1109/CCAA.2017.8230010. 978-1-5090-6471-7. (1252-1257). http://ieeexplore.ieee.org/document/8230010/ 340. Barceló P and Muñoz P. (2017). Graph Logics with Rational Relations. ACM Transactions on Computational Logic. 18:2. (1-41). Online publication date: 30-Apr-2017. https://doi.org/10.1145/3070822 341. Zhang X. On the Primitivity of SPARQL 1.1 Operators. Proceedings of the 26th International Conference on World Wide Web Companion. (875-876). https://doi.org/10.1145/3041021.3054260 342. Niewiadomski A, Penczek W, Skaruz J, Szreter M and Prola A. (2017). Combining ontology reductions with new approaches to automated abstract planning of Planics. Applied Soft Computing. 53:C. (352-379). Online publication date: 1-Apr-2017. https://doi.org/10.1016/j.asoc.2017.01.007 343. Barakat O, Koll D and Fu X. (2017). Gavel: Software-defined network control with graph databases 2017 20th Conference on Innovations in Clouds, Internet and Networks (ICIN). 10.1109/ICIN.2017.7899425. 978-1-5090-3672-1. (279-286). http://ieeexplore.ieee.org/document/7899425/ 344. Zhu Y, Yan E and Song I. (2017). The use of a graph-based system to improve bibliographic information retrieval. Journal of the Association for Information Science and Technology. 68:2. (480-490). Online publication date: 1-Feb-2017. https://doi.org/10.1002/asi.23677 345. Yoon B, Kim S and Kim S. (2017). Use of Graph Database for the Integration of Heterogeneous Biological Data. Genomics & Informatics. 10.5808/GI.2017.15.1.19. 15:1. (19). http://genominfo.org/journal/view.php?doi=10.5808/GI.2017.15.1.19 346. Lampoltshammer T, Guadamuz A, Wass C and Heistracher T. Openlaws.eu. Achieving Open Justice through Citizen Participation and Transparency. 10.4018/978-1-5225-0717-8.ch009. (173-190). http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-0717-8.ch009 347. Ma X, Beaulieu S, Fu L, Fox P, Di Stefano M and West P. Documenting Provenance for Reproducible Marine Ecosystem Assessment in Open Science. Oceanographic and Marine Cross-Domain Data Management for Sustainable Development. 10.4018/978-1-5225-0700-0.ch005. (100-126). http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-0700-0.ch005 348. SCHULTZ P and WISNESKY R. (2017). Algebraic data integration. Journal of Functional Programming. 10.1017/S0956796817000168. 27. https://www.cambridge.org/core/product/identifier/S0956796817000168/type/journal_article 349. Tuteja S and Kumar R. (2017). A System Architecture for Mapping Application Data into Complex Graph. Information, Communication and Computing Technology. 10.1007/978-981-10-6544-6_15. (148-155). http://link.springer.com/10.1007/978-981-10-6544-6_15 350. Jimenez S and Dueñas G. (2017). G-WordNet: Moving WordNet 3.0 and Its Resources to a Graph Database. Advances in Computing. 10.1007/978-3-319-66562-7_8. (100-114). http://link.springer.com/10.1007/978-3-319-66562-7_8 351. Manica E, Dorneles C and Galante R. (2017). Orion: A Cypher-Based Web Data Extractor. Database and Expert Systems Applications. 10.1007/978-3-319-64468-4_21. (275-289). https://link.springer.com/10.1007/978-3-319-64468-4_21 352. Furtado P. (2017). Scalability and Realtime on Big Data, MapReduce, NoSQL and Spark. Business Intelligence. 10.1007/978-3-319-61164-8_4. (79-104). http://link.springer.com/10.1007/978-3-319-61164-8_4 353. Voigt H. (2017). Declarative Multidimensional Graph Queries. Business Intelligence. 10.1007/978-3-319-61164-8_1. (1-37). http://link.springer.com/10.1007/978-3-319-61164-8_1 354. Lebedev A, Lee J, Rivera V and Mazzara M. (2017). Link Prediction Using Top-k Shortest Distances. Data Analytics. 10.1007/978-3-319-60795-5_10. (101-105). https://link.springer.com/10.1007/978-3-319-60795-5_10 355. Zhou F, Xu Z, Li Y, Xu J and Peng S. (2017). Private Graph Intersection Protocol. Information Security and Privacy. 10.1007/978-3-319-59870-3_13. (235-248). http://link.springer.com/10.1007/978-3-319-59870-3_13 356. Goz F and Mutlu A. (2017). Concept Discovery in Graph Databases. Hybrid Artificial Intelligent Systems. 10.1007/978-3-319-59650-1_6. (63-74). http://link.springer.com/10.1007/978-3-319-59650-1_6 357. Lombardi T. (2017). Macroanalysis in the Arts and Sciences. New Directions for Computing Education. 10.1007/978-3-319-54226-3_6. (87-100). http://link.springer.com/10.1007/978-3-319-54226-3_6 358. Akid H and Ben Ayed M. (2017). Towards NoSQL Graph Data Warehouse for Big Social Data Analysis. Intelligent Systems Design and Applications. 10.1007/978-3-319-53480-0_95. (965-973). http://link.springer.com/10.1007/978-3-319-53480-0_95 359. Reutter J and Vrgoč D. (2017). Navigational and Rule-Based Languages for Graph Databases. Reasoning Web: Logical Foundation of Knowledge Graph Construction and Query Answering. 10.1007/978-3-319-49493-7_3. (90-123). https://link.springer.com/10.1007/978-3-319-49493-7_3 360. Khan A and Ranu S. (2017). Big-Graphs: Querying, Mining, and Beyond. Handbook of Big Data Technologies. 10.1007/978-3-319-49340-4_16. (531-582). http://link.springer.com/10.1007/978-3-319-49340-4_16 361. Junghanns M, Petermann A, Neumann M and Rahm E. (2017). Management and Analysis of Big Graph Data: Current Systems and Open Challenges. Handbook of Big Data Technologies. 10.1007/978-3-319-49340-4_14. (457-505). http://link.springer.com/10.1007/978-3-319-49340-4_14 362. Küçükkeçeci C and Yazıcı A. (2017). A Graph-Based Big Data Model for Wireless Multimedia Sensor Networks. Advances in Big Data. 10.1007/978-3-319-47898-2_22. (205-215). https://link.springer.com/10.1007/978-3-319-47898-2_22 363. Shvorob I. (2017). New Approach for Saving Semistructured Medical Data. Advances in Intelligent Systems and Computing. 10.1007/978-3-319-45991-2_3. (29-40). http://link.springer.com/10.1007/978-3-319-45991-2_3 364. Wood P. (2017). Graph Database. Encyclopedia of Database Systems. 10.1007/978-1-4899-7993-3_183-2. (1-4). http://link.springer.com/10.1007/978-1-4899-7993-3_183-2 365. Gupta A. (2017). Graph Data Management in Scientific Applications. Encyclopedia of Database Systems. 10.1007/978-1-4899-7993-3_1298-2. (1-4). http://link.springer.com/10.1007/978-1-4899-7993-3_1298-2 366. San Martín M and Gutierrez C. (2017). Transforming Social Networks Data. Encyclopedia of Social Network Analysis and Mining. 10.1007/978-1-4614-7163-9_389-1. (1-13). http://link.springer.com/10.1007/978-1-4614-7163-9_389-1 367. Cerinšek M and Batagelj V. (2017). Sources of Network Data. Encyclopedia of Social Network Analysis and Mining. 10.1007/978-1-4614-7163-9_313-1. (1-9). http://link.springer.com/10.1007/978-1-4614-7163-9_313-1 368. Ueta K, Xue X, Nakamoto Y and Murakami S. (2016). A Distributed Graph Database for the Data Management of IoT Systems 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). 10.1109/iThings-GreenCom-CPSCom-SmartData.2016.74. 978-1-5090-5880-8. (299-304). http://ieeexplore.ieee.org/document/7917102/ 369. Castelltort A and Laurent A. (2016). Rogue behavior detection in NoSQL graph databases. Journal of Innovation in Digital Ecosystems. 10.1016/j.jides.2016.10.004. 3:2. (70-82). Online publication date: 1-Dec-2016. https://linkinghub.elsevier.com/retrieve/pii/S2352664516300177 370. Kuzmin K, Lu X, Mukherjee P, Zhuang J, Gaiteri C and Szymanski B. (2016). Supporting novel biomedical research via multilayer collaboration networks. Applied Network Science. 10.1007/s41109-016-0015-y. 1:1. Online publication date: 1-Dec-2016. https://appliednetsci.springeropen.com/articles/10.1007/s41109-016-0015-y 371. Emmanuel I and Stanier C. Defining Big Data. Proceedings of the International Conference on Big Data and Advanced Wireless Technologies. (1-6). https://doi.org/10.1145/3010089.3010090 372. Zhang X, Feng Z, Wang X, Rao G and Wu W. Context-Free Path Queries on RDF Graphs. The Semantic Web – ISWC 2016. (632-648). https://doi.org/10.1007/978-3-319-46523-4_38 373. (2016). Approximation and relaxation of semantic web path queries. Web Semantics: Science, Services and Agents on the World Wide Web. 40:C. (1-21). Online publication date: 1-Oct-2016. https://doi.org/10.1016/j.websem.2016.08.001 374. Dai D, Carns P, Ross R, Jenkins J, Muirhead N and Chen Y. (2016). An asynchronous traversal engine for graph-based rich metadata management. Parallel Computing. 58:C. (140-156). Online publication date: 1-Oct-2016. https://doi.org/10.1016/j.parco.2016.06.002 375. Fionda V, Gutierrez C and Pirrò G. (2016). Building knowledge maps of Web graphs. Artificial Intelligence. 239:C. (143-167). Online publication date: 1-Oct-2016. https://doi.org/10.1016/j.artint.2016.07.003 376. Weale T, Gadepally V, Hutchison D and Kepner J. (2016). Benchmarking the graphulo processing framework 2016 IEEE High Performance Extreme Computing Conference (HPEC). 10.1109/HPEC.2016.7761640. 978-1-5090-3525-0. (1-5). http://ieeexplore.ieee.org/document/7761640/ 377. Ma Z, Capretz M and Yan L. (2016). Storing massive Resource Description Framework (RDF) data: a survey. The Knowledge Engineering Review. 10.1017/S0269888916000217. 31:4. (391-413). Online publication date: 1-Sep-2016. https://www.cambridge.org/core/product/identifier/S0269888916000217/type/journal_article 378. Ravve E. (2016). Incremental computations over strongly distributed databases. Concurrency and Computation: Practice & Experience. 28:11. (3061-3076). Online publication date: 10-Aug-2016. https://doi.org/10.1002/cpe.3597 379. Kahng M, Navathe S, Stasko J and Chau D. (2016). Interactive browsing and navigation in relational databases. Proceedings of the VLDB Endowment. 9:12. (1017-1028). Online publication date: 1-Aug-2016. https://doi.org/10.14778/2994509.2994520 380. Zhang H, Lu F and Xu J. (2016). Modeling and Querying Moving Objects with Social Relationships. ISPRS International Journal of Geo-Information. 10.3390/ijgi5070121. 5:7. (121). http://www.mdpi.com/2220-9964/5/7/121 381. Pokorný J. Conceptual and Database Modelling of Graph Databases. Proceedings of the 20th International Database Engineering & Applications Symposium. (370-377). https://doi.org/10.1145/2938503.2938547 382. Grabon M, Michaliszyn J, Otop J and Wieczorek P. Querying data graphs with arithmetical regular expressions. Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. (1088-1094). /doi/10.5555/3060621.3060772 383. Johnson T, Kanza Y, Lakshmanan L and Shkapenyuk V. Nepal. Proceedings of the 1st ACM SIGMOD Workshop on Network Data Analytics. (1-8). https://doi.org/10.1145/2980523.2980530 384. Junghanns M, Petermann A, Teichmann N, Gómez K and Rahm E. Analyzing extended property graphs with Apache Flink. Proceedings of the 1st ACM SIGMOD Workshop on Network Data Analytics. (1-8). https://doi.org/10.1145/2980523.2980527 385. Zellweger H. (2016). Tree Visualizations in Structured Data Recursively Defined by the Aleph Data Relation 2016 20th International Conference Information Visualisation (IV). 10.1109/IV.2016.75. 978-1-4673-8942-6. (21-26). http://ieeexplore.ieee.org/document/7557898/ 386. Ben Ammar A. (2016). Graph database partitioning: A study 2016 7th International Conference on Information, Intelligence, Systems & Applications (IISA). 10.1109/IISA.2016.7785355. 978-1-5090-3429-1. (1-9). http://ieeexplore.ieee.org/document/7785355/ 387. Shang Z, Li F, Yu J, Zhang Z and Cheng H. Graph Analytics Through Fine-Grained Parallelism. Proceedings of the 2016 International Conference on Management of Data. (463-478). https://doi.org/10.1145/2882903.2915238 388. Cohen S. Data Management for Social Networking. Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems. (165-177). https://doi.org/10.1145/2902251.2902306 389. Benzi K, Ricaud B and Vandergheynst P. Principal Patterns on Graphs: Discovering Coherent Structures in Datasets. IEEE Transactions on Signal and Information Processing over Networks. 10.1109/TSIPN.2016.2524500. 2:2. (160-173). http://ieeexplore.ieee.org/document/7398168/ 390. Mohan A, Ebrahimi M, Lu S and Kotov A. (2016). A NoSQL Data Model for Scalable Big Data Workflow Execution 2016 IEEE International Congress on Big Data (BigData Congress). 10.1109/BigDataCongress.2016.15. 978-1-5090-2622-7. (52-59). http://ieeexplore.ieee.org/document/7584920/ 391. Abdelhamid S, Kuhlman C, Marathe M and Ravi S. (2016). Network Services and Their Compositions for Network Science Applications. Procedia Computer Science. 80:C. (472-483). Online publication date: 1-Jun-2016. https://doi.org/10.1016/j.procs.2016.05.326 392. Sharma S. (2016). Expanded cloud plumes hiding Big Data ecosystem. Future Generation Computer Systems. 59:C. (63-92). Online publication date: 1-Jun-2016. https://doi.org/10.1016/j.future.2016.01.003 393. Libkin L, Martens W and Vrgoč D. (2016). Querying Graphs with Data. Journal of the ACM. 63:2. (1-53). Online publication date: 4-May-2016. https://doi.org/10.1145/2850413 394. Gadepally V, Kepner J and Reuther A. (2016). Storage and Database Management for Big Data. Big Data. 10.1201/b19694-4. (15-41). Online publication date: 3-May-2016. http://www.crcnetbase.com/doi/10.1201/b19694-4 395. Raji P and Surendran S. (2016). RDF approach on social network analysis 2016 International Conference on Research Advances in Integrated Navigation Systems (RAINS). 10.1109/RAINS.2016.7764416. 978-1-5090-1111-7. (1-4). http://ieeexplore.ieee.org/document/7764416/ 396. Chavarria-Miranda D, Castellana V, Morari A, Haglin D and Feo J. (2016). GraQL: A Query Language for High-Performance Attributed Graph Databases 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). 10.1109/IPDPSW.2016.216. 978-1-5090-3682-0. (1453-1462). http://ieeexplore.ieee.org/document/7530036/ 397. Singh K and Singh V. (2016). Answering graph pattern query using incremental views 2016 International Conference on Computing, Communication and Automation (ICCCA). 10.1109/CCAA.2016.7813689. 978-1-5090-1666-2. (54-59). http://ieeexplore.ieee.org/document/7813689/ 398. Ramesh D, Sinha A and Singh S. (2016). Data modelling for discrete time series data using Cassandra and MongoDB 2016 3rd International Conference on Recent Advances in Information Technology (RAIT). 10.1109/RAIT.2016.7507966. 978-1-4799-8579-1. (598-601). http://ieeexplore.ieee.org/document/7507966/ 399. Daltio J and Medeiros C. (2016). A View Handler for Semantic Graphs 2016 IEEE Tenth International Conference on Semantic Computing (ICSC). 10.1109/ICSC.2016.32. 978-1-5090-0662-5. (238-241). http://ieeexplore.ieee.org/document/7439339/ 400. Inoue T, Iwashita H, Kawahara J and Minato S. (2016). Graphillion. International Journal on Software Tools for Technology Transfer (STTT). 18:1. (57-66). Online publication date: 1-Feb-2016. https://doi.org/10.1007/s10009-014-0352-z 401. Orel O, Zakošek S and Baranovič M. (2017). Property Oriented Relational-To-Graph Database Conversion. Automatika. 10.7305/automatika.2017.02.1581. 57:3. (836-845). Online publication date: 1-Jan-2016. https://www.tandfonline.com/doi/full/10.7305/automatika.2017.02.1581 402. Barceló P, Romero M and Vardi M. (2016). Semantic Acyclicity on Graph Databases. SIAM Journal on Computing. 10.1137/15M1034714. 45:4. (1339-1376). Online publication date: 1-Jan-2016. http://epubs.siam.org/doi/10.1137/15M1034714 403. Singh K and Singh V. (2016). MoVie: A scalable algorithm for answering graph pattern query using incremental views 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC). 10.1109/PDGC.2016.7913116. 978-1-5090-3669-1. (61-66). http://ieeexplore.ieee.org/document/7913116/ 404. Patel A and Dharwa J. (2016). An integrated hybrid recommendation model using graph database 2016 International Conference on ICT in Business Industry & Government (ICTBIG). 10.1109/ICTBIG.2016.7892680. 978-1-5090-5515-9. (1-5). http://ieeexplore.ieee.org/document/7892680/ 405. Noel S, Harley E, Tam K, Limiero M and Share M. (2016). CyGraph. Cognitive Computing: Theory and Applications. 10.1016/bs.host.2016.07.001. (117-167). https://linkinghub.elsevier.com/retrieve/pii/S0169716116300426 406. Zhai X, Pan H, Xie X, Zhang Z and Han Q. (2016). Storage and Parallel Loading System Based on Mode Network for Multimode Medical Image Data. Social Computing. 10.1007/978-981-10-2098-8_25. (211-216). http://link.springer.com/10.1007/978-981-10-2098-8_25 407. He X and Yang B. (2016). An Improved Keyword Search on Big Data Graph with Graphics Processors. Computational Intelligence and Intelligent Systems. 10.1007/978-981-10-0356-1_41. (390-397). http://link.springer.com/10.1007/978-981-10-0356-1_41 408. Meier A and Kaufmann M. (2016). NoSQL-Datenbanken. SQL- & NoSQL-Datenbanken. 10.1007/978-3-662-47664-2_7. (221-240). http://link.springer.com/10.1007/978-3-662-47664-2_7 409. Cignoni G and Cossu G. (2016). The Global Virtual Museum of Information Science & Technology, a Project Idea. International Communities of Invention and Innovation. 10.1007/978-3-319-49463-0_7. (101-114). http://link.springer.com/10.1007/978-3-319-49463-0_7 410. Dang M and Nguyen T. (2016). Using Graph Database for Evidence Correlation on Android Smartphones. Future Data and Security Engineering. 10.1007/978-3-319-48057-2_15. (209-216). http://link.springer.com/10.1007/978-3-319-48057-2_15 411. Kaufmann M, Waldis A, Siegfried P, Wilke G, Portmann E and Hemmje M. (2016). Intuitive Knowledge Connectivity: Design and Prototyping of Cross-Platform Knowledge Networks. Knowledge Science, Engineering and Management. 10.1007/978-3-319-47650-6_27. (337-348). http://link.springer.com/10.1007/978-3-319-47650-6_27 412. Sayah T, Coquery E, Thion R and Hacid M. (2016). Access Control Enforcement for Selective Disclosure of Linked Data. Security and Trust Management. 10.1007/978-3-319-46598-2_4. (47-63). http://link.springer.com/10.1007/978-3-319-46598-2_4 413. Warnke T and Uhrmacher A. (2016). Spatiotemporal Pattern Matching in RoboCup. Multiagent System Technologies. 10.1007/978-3-319-45889-2_7. (89-104). http://link.springer.com/10.1007/978-3-319-45889-2_7 414. Pivert O, Slama O and Thion V. (2016). Fuzzy Quantified Structural Queries to Fuzzy Graph Databases. Scalable Uncertainty Management. 10.1007/978-3-319-45856-4_18. (260-273). http://link.springer.com/10.1007/978-3-319-45856-4_18 415. Spyratos N and Sugibuchi T. (2016). PROPER - A Graph Data Model Based on Property Graphs. Information Search, Integration, and Personalization. 10.1007/978-3-319-43862-7_2. (23-45). http://link.springer.com/10.1007/978-3-319-43862-7_2 416. Laurent D. (2016). On Monotonic Deductive Database Updating Under the Open World Assumption. Information Search, Integration, and Personalization. 10.1007/978-3-319-43862-7_1. (3-22). http://link.springer.com/10.1007/978-3-319-43862-7_1 417. Zhang H, Lu F and Chen J. (2016). A Geo-Social Data Model for Moving Objects. Data Mining and Big Data. 10.1007/978-3-319-40973-3_11. (115-122). http://link.springer.com/10.1007/978-3-319-40973-3_11 418. Strobin L and Niewiadomski A. (2016). Integration of Multiple Graph Datasets and Their Linguistic Summaries: An Application to Linked Data. Artificial Intelligence and Soft Computing. 10.1007/978-3-319-39378-0_29. (333-343). http://link.springer.com/10.1007/978-3-319-39378-0_29 419. Castelltort A and Laurent A. (2016). Extracting Fuzzy Summaries from NoSQL Graph Databases. Flexible Query Answering Systems 2015. 10.1007/978-3-319-26154-6_15. (189-200). http://link.springer.com/10.1007/978-3-319-26154-6_15 420. Di Mascio T, Gobbo F and Tarantino L. (2016). Requirements and Open Issues for ISs Supporting Dynamic Community Bonding in Emergency Situations. Empowering Organizations. 10.1007/978-3-319-23784-8_20. (257-271). https://link.springer.com/10.1007/978-3-319-23784-8_20 421. Wang S, Jin J, Rivett P and Kitazawa A. (2015). Technical Survey Graph Databases and Applications . International Journal of Semantic Computing. 10.1142/S1793351X15500129. 09:04. (523-545). Online publication date: 1-Dec-2015. http://www.worldscientific.com/doi/abs/10.1142/S1793351X15500129 422. Pääkkönen P and Pakkala D. (2015). Reference Architecture and Classification of Technologies, Products and Services for Big Data Systems. Big Data Research. 2:4. (166-186). Online publication date: 1-Dec-2015. https://doi.org/10.1016/j.bdr.2015.01.001 423. Cañas C, Pacheco E, Kemme B, Kienzle J and Jacobsen H. GraPS. Proceedings of the 16th Annual Middleware Conference. (1-12). https://doi.org/10.1145/2814576.2814812 424. Makris C and Theodoridis E. (2015). Computational Methods for Modeling Biological Interaction Networks. Pattern Recognition in Computational Molecular Biology. 10.1002/9781119078845.ch26. (505-524). Online publication date: 19-Nov-2015. https://onlinelibrary.wiley.com/doi/10.1002/9781119078845.ch26 425. Libkin L, Tan T and Vrgoč D. (2015). Regular expressions for data words. Journal of Computer and System Sciences. 81:7. (1278-1297). Online publication date: 1-Nov-2015. https://doi.org/10.1016/j.jcss.2015.03.005 426. Rodriguez M. The Gremlin graph traversal machine and language (invited talk). Proceedings of the 15th Symposium on Database Programming Languages. (1-10). https://doi.org/10.1145/2815072.2815073 427. George K and Mathew T. Big database stores a review on various big data datastores. Proceedings of the 2015 International Conference on Green Computing and Internet of Things (ICGCIoT). (567-573). https://doi.org/10.1109/ICGCIoT.2015.7380529 428. Islam M, Chengfei Liu and Jianxin Li . (2015). Efficient Answering of Why-Not Questions in Similar Graph Matching. IEEE Transactions on Knowledge and Data Engineering. 27:10. (2672-2686). Online publication date: 1-Oct-2015. https://doi.org/10.1109/TKDE.2015.2432798 429. Surinx D, Fletcher G, Gyssens M, Leinders D, Van den Bussche J, Van Gucht D, Vansummeren S and Wu Y. (2015). Relative expressive power of navigational querying on graphs using transitive closure. Logic Journal of IGPL. 10.1093/jigpal/jzv028. 23:5. (759-788). Online publication date: 1-Oct-2015. https://academic.oup.com/jigpal/article-lookup/doi/10.1093/jigpal/jzv028 430. Kendea M, Gkantouna V, Rapti A, Sioutas S, Tzimas G and Tsolis D. Graph DBs vs. Column-Oriented Stores. Revised Selected Papers of the First International Workshop on Algorithmic Aspects of Cloud Computing - Volume 9511. (62-74). https://doi.org/10.1007/978-3-319-29919-8_5 431. Dai D, Carns P, Ross R, Jenkins J, Blauer K and Chen Y. GraphTrek. Proceedings of the 2015 IEEE International Conference on Cluster Computing. (284-293). https://doi.org/10.1109/CLUSTER.2015.48 432. Pabón M and Collazos C. A Visual Query Language for Data Graphs. Proceedings of the XVI International Conference on Human Computer Interaction. (1-2). https://doi.org/10.1145/2829875.2829918 433. Ravikumar G and Khaparde S. (2015). CIM oriented graph database for network topology processing and applications integration 2015 50th International Universities Power Engineering Conference (UPEC). 10.1109/UPEC.2015.7339843. 978-1-4673-9682-0. (1-7). http://ieeexplore.ieee.org/document/7339843/ 434. Bronselaer A, Van Britsom D and De Tré G. (2015). Pointwise multi-valued fusion. Information Fusion. 25:C. (121-133). Online publication date: 1-Sep-2015. https://doi.org/10.1016/j.inffus.2014.10.001 435. Cavoto P, Cardoso V, Lebbe R and Santanchè A. FishGraph. Proceedings of the 2015 IEEE 11th International Conference on e-Science. (177-186). https://doi.org/10.1109/eScience.2015.61 436. Roughan M and Tuke J. The Hitchhikers Guide to Sharing Graph Data. Proceedings of the 2015 3rd International Conference on Future Internet of Things and Cloud. (435-442). https://doi.org/10.1109/FiCloud.2015.76 437. Nakamoto Y. (2015). Exploring a Uniform Framework for a Mobile Collaborative Work Support Platform 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom). 10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.109. 978-1-4673-7211-4. (537-542). http://ieeexplore.ieee.org/document/7518288/ 438. Pivert O, Smits G and Thion V. (2015). Expression and efficient processing of fuzzy queries in a graph database context 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). 10.1109/FUZZ-IEEE.2015.7337849. 978-1-4673-7428-6. (1-8). http://ieeexplore.ieee.org/document/7337849/ 439. Figueira D and Libkin L. (2015). Synchronizing Relations on Words. Theory of Computing Systems. 57:2. (287-318). Online publication date: 1-Aug-2015. https://doi.org/10.1007/s00224-014-9584-2 440. Lampoltshammer T and Wiegand S. (2015). Improving the Computational Performance of Ontology-Based Classification Using Graph Databases. Remote Sensing. 10.3390/rs70709473. 7:7. (9473-9491). http://www.mdpi.com/2072-4292/7/7/9473 441. Dehghani R and Ramsin R. (2015). Methodologies for developing knowledge management systems: an evaluation framework. Journal of Knowledge Management. 10.1108/JKM-10-2014-0438. 19:4. (682-710). Online publication date: 13-Jul-2015. http://www.emeraldinsight.com/doi/10.1108/JKM-10-2014-0438 442. Figueira D and Libkin L. Path Logics for Querying Graphs. Proceedings of the 2015 30th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS). (329-340). https://doi.org/10.1109/LICS.2015.39 443. Abay N, Mutlu A and Karagoz P. (2015). A path-finding based method for concept discovery in graphs 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA). 10.1109/IISA.2015.7388092. 978-1-4673-9311-9. (1-6). http://ieeexplore.ieee.org/document/7388092/ 444. Sun R, Zhang L, Chen Z and Hao Z. A Balanced Vertex Cut Partition Method in Distributed Graph Computing. Revised Selected Papers, Part II, of the 5th International Conference on Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques - Volume 9243. (43-54). https://doi.org/10.1007/978-3-319-23862-3_5 445. Przyjaciel-Zablocki M, Schätzle A and Lausen G. TriAL-QL. Proceedings of the 18th International Workshop on Web and Databases. (48-54). https://doi.org/10.1145/2767109.2767115 446. Van Gucht D, Williams R, Woodruff D and Zhang Q. The Communication Complexity of Distributed Set-Joins with Applications to Matrix Multiplication. Proceedings of the 34th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems. (199-212). https://doi.org/10.1145/2745754.2745779 447. Berro A, Megdiche I and Teste O. (2015). Holistic Statistical Open Data integration based on integer linear programming 2015 IEEE 9th International Conference on Research Challenges in Information Science (RCIS). 10.1109/RCIS.2015.7128908. 978-1-4673-6630-4. (468-479). http://ieeexplore.ieee.org/document/7128908/ 448. Uyar A and Aliyu F. (2015). (2015). Evaluating search features of Google Knowledge Graph and Bing Satori. Online Information Review. 10.1108/OIR-10-2014-0257. 39:2. (197-213). Online publication date: 13-Apr-2015.. Online publication date: 13-Apr-2015. https://www.emerald.com/insight/content/doi/10.1108/OIR-10-2014-0257/full/html 449. Fletcher G, Gyssens M, Leinders D, Surinx D, Van den Bussche J, Van Gucht D, Vansummeren S and Wu Y. (2015). Relative expressive power of navigational querying on graphs. Information Sciences: an International Journal. 298:C. (390-406). Online publication date: 20-Mar-2015. https://doi.org/10.1016/j.ins.2014.11.031 450. Lim S, Lee S, Ganesh G, Brown T and Sukumar S. (2015). Graph Processing Platforms at Scale: Practices and Experiences 2015 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS). 10.1109/ISPASS.2015.7095783. 978-1-4799-1957-4. (42-51). http://ieeexplore.ieee.org/document/7095783/ 451. Goyal V, Agarwal A, Pilli E and Mehta S. (2015). Information integration for movies data using graph database 2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES). 10.1109/SPICES.2015.7091523. 978-1-4799-1823-2. (1-5). http://ieeexplore.ieee.org/document/7091523/ 452. Fionda V, Pirrò G and Gutierrez C. (2015). NautiLOD. ACM Transactions on the Web. 9:1. (1-43). Online publication date: 23-Jan-2015. https://doi.org/10.1145/2697393 453. Henkel R, Wolkenhauer O and Waltemath D. (2015). Combining computational models, semantic annotations and simulation experiments in a graph database. Database. 10.1093/database/bau130. 2015. Online publication date: 1-Jan-2015. https://academic.oup.com/database/article/doi/10.1093/database/bau130/2433130 454. Urma R and Mycroft A. (2015). Source-code queries with graph databases-with application to programming language usage and evolution. Science of Computer Programming. 97:P1. (127-134). Online publication date: 1-Jan-2015. https://doi.org/10.1016/j.scico.2013.11.010 455. (2015). References. RDF Database Systems. 10.1016/B978-0-12-799957-9.00010-9. (227-233). https://linkinghub.elsevier.com/retrieve/pii/B9780127999579000109 456. Fletcher G, Gyssens M, Leinders D, Bussche J, Gucht D, Vansummeren S and Wu Y. (2015). The impact of transitive closure on the expressiveness of navigational query languages on unlabeled graphs. Annals of Mathematics and Artificial Intelligence. 73:1-2. (167-203). Online publication date: 1-Jan-2015. https://doi.org/10.1007/s10472-013-9346-x 457. Dimitrov D, Singh L and Mann J. (2015). Query Operators for Comparing Uncertain Graphs. Transactions on Large-Scale Data- and Knowledge-Centered Systems XVIII. 10.1007/978-3-662-46485-4_5. (115-152). https://link.springer.com/10.1007/978-3-662-46485-4_5 458. Pokorný J. (2015). Graph Databases: Their Power and Limitations. Computer Information Systems and Industrial Management. 10.1007/978-3-319-24369-6_5. (58-69). http://link.springer.com/10.1007/978-3-319-24369-6_5 459. Castelltort A and Laurent A. (2015). Fuzzy Historical Graph Pattern Matching A NoSQL Graph Database Approach for Fraud Ring Resolution. Artificial Intelligence Applications and Innovations. 10.1007/978-3-319-23868-5_11. (151-167). http://link.springer.com/10.1007/978-3-319-23868-5_11 460. Ghrab A, Romero O, Skhiri S, Vaisman A and Zimányi E. (2015). A Framework for Building OLAP Cubes on Graphs. Advances in Databases and Information Systems. 10.1007/978-3-319-23135-8_7. (92-105). https://link.springer.com/10.1007/978-3-319-23135-8_7 461. Lange M and Lozes E. (2015). Conjunctive Visibly-Pushdown Path Queries. Fundamentals of Computation Theory. 10.1007/978-3-319-22177-9_25. (327-338). https://link.springer.com/10.1007/978-3-319-22177-9_25 462. Blanco D, Arias A, Cañete V and Suárez J. (2015). OBEliSK: Novel Knowledgebase of Object Features and Exchange Strategies. Computer Vision - ECCV 2014 Workshops. 10.1007/978-3-319-16181-5_34. (448-454). http://link.springer.com/10.1007/978-3-319-16181-5_34 463. Trojer T, Farwick M, Häusler M and Breu R. (2015). Living Modeling of IT Architectures: Challenges and Solutions. Software, Services, and Systems. 10.1007/978-3-319-15545-6_26. (458-474). http://link.springer.com/10.1007/978-3-319-15545-6_26 464. Amirian P, Basiri A, Gales G, Winstanley A and McDonald J. (2015). The Next Generation of Navigational Services Using OpenStreetMap Data: The Integration of Augmented Reality and Graph Databases. OpenStreetMap in GIScience. 10.1007/978-3-319-14280-7_11. (211-228). https://link.springer.com/10.1007/978-3-319-14280-7_11 465. Berro A, Megdiche I and Teste O. (2015). A Content-Driven ETL Processes for Open Data. New Trends in Database and Information Systems II. 10.1007/978-3-319-10518-5_3. (29-40). https://link.springer.com/10.1007/978-3-319-10518-5_3 466. 467. Lukyanenko R, Parsons J and Wiersma Y. (2014). The IQ of the Crowd. Information Systems Research. 25:4. (669-689). Online publication date: 1-Dec-2014. https://doi.org/10.1287/isre.2014.0537 468. Mesiti M, Re M and Valentini G. (2014). Think globally and solve locally: secondary memory-based network learning for automated multi-species function prediction. GigaScience. 10.1186/2047-217X-3-5. 3:1. Online publication date: 1-Dec-2014. https://academic.oup.com/gigascience/article-lookup/doi/10.1186/2047-217X-3-5 469. Wu Y, Zhong Z, Xiong W and Jing N. (2014). Geo-Link: Correlations of Heterogeneous Geo-Spatial Entities. Arabian Journal for Science and Engineering. 10.1007/s13369-014-1475-y. 39:12. (8811-8824). Online publication date: 1-Dec-2014. http://link.springer.com/10.1007/s13369-014-1475-y 470. Sun Y and Jara A. (2014). An extensible and active semantic model of information organizing for the Internet of Things. Personal and Ubiquitous Computing. 18:8. (1821-1833). Online publication date: 1-Dec-2014. https://doi.org/10.1007/s00779-014-0786-z 471. Pivert O, Thion V, Jaudoin H and Smits G. On a Fuzzy Algebra for Querying Graph Databases. Proceedings of the 2014 IEEE 26th International Conference on Tools with Artificial Intelligence. (748-755). https://doi.org/10.1109/ICTAI.2014.116 472. Pinheiro R, Aires B, Araujo A, Holanda M, Walter M and Lifschitz S. (2014). Storing provenance data of genome project workflows using graph database 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 10.1109/BIBM.2014.6999292. 978-1-4799-5669-2. (16-22). http://ieeexplore.ieee.org/document/6999292/ 473. Kim K and Eijkhout V. (2014). A Parallel Sparse Direct Solver via Hierarchical DAG Scheduling. ACM Transactions on Mathematical Software. 41:1. (1-27). Online publication date: 27-Oct-2014. https://doi.org/10.1145/2629641 474. Hempe N, Waspe R and Rossmann J. Combining Complex Simulations with Realistic Virtual Testing Environments — The eRobotics-Approach for Semantics-Based Multi-domain VR Simulation Systems. Proceedings of the 4th International Conference on Simulation, Modeling, and Programming for Autonomous Robots - Volume 8810. (110-121). https://doi.org/10.1007/978-3-319-11900-7_10 475. Vaikuntam A and Perumal V. Evaluation of contemporary graph databases. Proceedings of the 7th ACM India Computing Conference. (1-10). https://doi.org/10.1145/2675744.2675752 476. Zheng Y, Capra L, Wolfson O and Yang H. (2014). Urban Computing. ACM Transactions on Intelligent Systems and Technology. 5:3. (1-55). Online publication date: 1-Oct-2014. https://doi.org/10.1145/2629592 477. Savkli C, Carr R, Chapman M, Chee B and Minch D. (2014). Socrates 2014 IEEE High Performance Extreme Computing Conference (HPEC). 10.1109/HPEC.2014.7040993. 978-1-4799-6233-4. (1-6). http://ieeexplore.ieee.org/document/7040993/ 478. Gröger C, Schwarz H and Mitschang B. The Deep Data Warehouse. Proceedings of the 2014 IEEE 18th International Enterprise Distributed Object Computing Conference. (210-217). https://doi.org/10.1109/EDOC.2014.36 479. Martinez S and Pavlich-Mariscal J. (2014). Formal design of a model repository based on knowledge representation using graphs 2014 9th Computing Colombian Conference (9CCC). 10.1109/ColumbianCC.2014.6955346. 978-1-4799-6717-9. (249-254). http://ieeexplore.ieee.org/document/6955346/ 480. Zhang X and Van den Bussche J. (2014). On the primitivity of operators in SPARQL. Information Processing Letters. 10.1016/j.ipl.2014.03.014. 114:9. (480-485). Online publication date: 1-Sep-2014. https://linkinghub.elsevier.com/retrieve/pii/S002001901400057X 481. Barceló P and Muñoz P. Graph logics with rational relations. Proceedings of the Joint Meeting of the Twenty-Third EACSL Annual Conference on Computer Science Logic (CSL) and the Twenty-Ninth Annual ACM/IEEE Symposium on Logic in Computer Science (LICS). (1-10). https://doi.org/10.1145/2603088.2603122 482. Sakr S, Elnikety S and He Y. (2014). Hybrid query execution engine for large attributed graphs. Information Systems. 41. (45-73). Online publication date: 1-May-2014. https://doi.org/10.1016/j.is.2013.10.007 483. Spyropoulou E, De Bie T and Boley M. (2014). Interesting pattern mining in multi-relational data. Data Mining and Knowledge Discovery. 28:3. (808-849). Online publication date: 1-May-2014. https://doi.org/10.1007/s10618-013-0319-9 484. Rezaei Mahdiraji A and Baumann P. Processing scientific mesh queries in graph databases. Proceedings of the 23rd International Conference on World Wide Web. (1163-1168). https://doi.org/10.1145/2567948.2580061 485. Ding Y, Zhou M, Zhao Z, Eisenstat S and Shen X. (2014). Finding the limit. ACM SIGARCH Computer Architecture News. 42:1. (607-622). Online publication date: 5-Apr-2014. https://doi.org/10.1145/2654822.2541945 486. Park Y, Shankar M, Park B and Ghosh J. (2014). Graph databases for large-scale healthcare systems: A framework for efficient data management and data services 2014 IEEE 30th International Conference on Data Engineering Workshops (ICDEW). 10.1109/ICDEW.2014.6818295. 978-1-4799-3481-2. (12-19). http://ieeexplore.ieee.org/document/6818295/ 487. Kendall-Morwick J and Leake D. (2014). Facilitating representation and retrieval of structured cases. Information Systems. 40. (106-114). Online publication date: 1-Mar-2014. https://doi.org/10.1016/j.is.2012.11.007 488. McColl R, Ediger D, Poovey J, Campbell D and Bader D. A performance evaluation of open source graph databases. Proceedings of the first workshop on Parallel programming for analytics applications. (11-18). https://doi.org/10.1145/2567634.2567638 489. Agarwal P and Owzar K. (2015). Next Generation Distributed Computing for Cancer Research. Cancer Informatics. 10.4137/CIN.S16344. 13s7. (CIN.S16344). Online publication date: 1-Jan-2014. http://journals.sagepub.com/doi/10.4137/CIN.S16344 490. Barceló P, Libkin L and Reutter J. (2014). Querying Regular Graph Patterns. Journal of the ACM. 61:1. (1-54). Online publication date: 1-Jan-2014. https://doi.org/10.1145/2559905 491. Ying X, Xin S and He Y. (2014). Parallel chen-han (PCH) algorithm for discrete geodesics. ACM Transactions on Graphics. 33:1. (1-11). Online publication date: 1-Jan-2014. https://doi.org/10.1145/2534161 492. Xu K, Cao Y, Ma L, Dong Z, Wang R and Hu S. (2014). A practical algorithm for rendering interreflections with all-frequency BRDFs. ACM Transactions on Graphics. 33:1. (1-16). Online publication date: 1-Jan-2014. https://doi.org/10.1145/2533687 493. Daraghmi E and Yuan S. (2014). We are so close, less than 4 degrees separating you and me!. Computers in Human Behavior. 30. (273-285). Online publication date: 1-Jan-2014. https://doi.org/10.1016/j.chb.2013.09.014 494. (2014). References. Digital Asset Ecosystems. 10.1016/B978-1-84334-716-3.50012-9. (155-176). https://linkinghub.elsevier.com/retrieve/pii/B9781843347163500129 495. Pandit D, Chaki N and Chattopadhyay S. (2014). Hyper Object Data Model: A Simple Data Model for Handling Semi-Structured Data. Emerging Trends in Computing and Communication. 10.1007/978-81-322-1817-3_32. (315-325). https://link.springer.com/10.1007/978-81-322-1817-3_32 496. Kendall-Morwick J and Leake D. (2014). A Study of Two-Phase Retrieval for Process-Oriented Case-Based Reasoning. Successful Case-based Reasoning Applications-2. 10.1007/978-3-642-38736-4_2. (7-27). https://link.springer.com/10.1007/978-3-642-38736-4_2 497. Daltio J and Medeiros C. (2014). Handling Multiple Foci in Graph Databases. Data Integration in the Life Sciences. 10.1007/978-3-319-08590-6_6. (58-65). http://link.springer.com/10.1007/978-3-319-08590-6_6 498. Jankun-Kelly T, Dwyer T, Holten D, Hurter C, Nöllenburg M, Weaver C and Xu K. (2014). Scalability Considerations for Multivariate Graph Visualization. Multivariate Network Visualization. 10.1007/978-3-319-06793-3_10. (207-235). http://link.springer.com/10.1007/978-3-319-06793-3_10 499. Wycislik L and Warchal L. (2014). A Performance Comparison of Several Common Computation Tasks Used in Social Network Analysis Performed on Graph and Relational Databases. Man-Machine Interactions 3. 10.1007/978-3-319-02309-0_70. (651-659). https://link.springer.com/10.1007/978-3-319-02309-0_70 500. Navarro L, Appel A and Junior E. (2014). GraphDB – Storing Large Graphs on Secondary Memory. New Trends in Databases and Information Systems. 10.1007/978-3-319-01863-8_20. (177-186). https://link.springer.com/10.1007/978-3-319-01863-8_20 501. San Martín M and Gutierrez C. (2014). Transforming and Integrating Social Networks and Social Media Data. Encyclopedia of Social Network Analysis and Mining. 10.1007/978-1-4614-6170-8_389. (2202-2214). http://link.springer.com/10.1007/978-1-4614-6170-8_389 502. Cerinšek M and Batagelj V. (2014). Sources of Network Data. Encyclopedia of Social Network Analysis and Mining. 10.1007/978-1-4614-6170-8_313. (1946-1954). http://link.springer.com/10.1007/978-1-4614-6170-8_313 503. Baglioni M, Pieroni S, Geraci F, Mariani F, Molinaro S, Pellegrini M and Lastres E. (2013). A New Framework for Distilling Higher Quality Information from Health Data via Social Network Analysis 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW). 10.1109/ICDMW.2013.142. 978-1-4799-3142-2. (48-55). http://ieeexplore.ieee.org/document/6753902/ 504. Lukyanenko R and Parsons J. Is Traditional Conceptual Modeling Becoming Obsolete?. Proceedings of the 32nd International Conference on Conceptual Modeling - Volume 8217. (61-73). https://doi.org/10.1007/978-3-642-41924-9_6 505. Bao Z, Tay Y and Zhou J. sonSchema. Proceedings of the 32nd International Conference on Conceptual Modeling - Volume 8217. (197-211). https://doi.org/10.1007/978-3-642-41924-9_18 506. (2013). Next-Generation Field Guides. BioScience. 10.1525/bio.2013.63.11.8. 63:11. (891-899). Online publication date: 1-Nov-2013. https://academic.oup.com/bioscience/article-lookup/doi/10.1525/bio.2013.63.11.8 507. Fionda V and Pirro' G. Querying graphs with preferences. Proceedings of the 22nd ACM international conference on Information & Knowledge Management. (929-938). https://doi.org/10.1145/2505515.2505758 508. Rezaei Mahdiraji A, Baumann P and Berti G. ImG-complex. Proceedings of the 22nd ACM international conference on Information & Knowledge Management. (1619-1624). https://doi.org/10.1145/2505515.2505733 509. Hu B, Carvalho N and Matsutsuka T. (2013). Towards Big Linked Data. International Journal of Data Warehousing and Mining. 9:4. (19-43). Online publication date: 1-Oct-2013. https://doi.org/10.4018/ijdwm.2013100102 510. Jouili S and Reynaga A. imGraph. Proceedings of the 2013 International Conference on Social Computing. (732-737). https://doi.org/10.1109/SocialCom.2013.109 511. Castelltort A and Laurent A. (2013). Representing history in graph-oriented NoSQL databases: A versioning system 2013 Eighth International Conference on Digital Information Management (ICDIM). 10.1109/ICDIM.2013.6694022. 978-1-4799-0615-4. (228-234). http://ieeexplore.ieee.org/document/6694022/ 512. Mahdiraji A and Baumann P. (2013). Database support for unstructured meshes. Proceedings of the VLDB Endowment. 6:12. (1404-1409). Online publication date: 28-Aug-2013. https://doi.org/10.14778/2536274.2536326 513. Rapti A, Theodoridis E and Tsakalidis A. Evaluation of Protein-Protein Interaction Management Systems. Proceedings of the 2013 24th International Workshop on Database and Expert Systems Applications. (100-104). https://doi.org/10.1109/DEXA.2013.39 514. Ghrab A, Skhiri S, Jouili S and Zimányi E. An Analytics-Aware Conceptual Model for Evolving Graphs. Proceedings of the 15th International Conference on Data Warehousing and Knowledge Discovery - Volume 8057. (1-12). https://doi.org/10.1007/978-3-642-40131-2_1 515. Zhang Q, Chen Z, Lv A, Zhao L, Liu F and Zou J. A Universal Storage Architecture for Big Data in Cloud Environment. Proceedings of the 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing. (476-480). https://doi.org/10.1109/GreenCom-iThings-CPSCom.2013.96 516. Yang S, Tang W and He B. (2013). A Parallel Query Processing Technique for Keyword Search over Data Graph. Applied Mechanics and Materials. 10.4028/www.scientific.net/AMM.380-384.2609. 380-384. (2609-2613). https://www.scientific.net/AMM.380-384.2609 517. Halpin H and Mcneill F. (2013). Discovering meaning on the go in large heterogenous data. Artificial Intelligence Review. 40:2. (107-126). Online publication date: 1-Aug-2013. https://doi.org/10.1007/s10462-012-9377-4 518. Mazuran M, Serra E and Zaniolo C. (2013). Extending the power of datalog recursion. The VLDB Journal — The International Journal on Very Large Data Bases. 22:4. (471-493). Online publication date: 1-Aug-2013. https://doi.org/10.1007/s00778-012-0299-1 519. Poulovassilis A. Database research challenges and opportunities of big graph data. Proceedings of the 29th British National conference on Big Data. (29-32). https://doi.org/10.1007/978-3-642-39467-6_6 520. Schmitt O and Majchrzak T. (2013). Document-Based Databases for Medical Information Systems and Crisis Management. International Journal of Information Systems for Crisis Response and Management. 10.4018/ijiscram.2013070104. 5:3. (63-80). Online publication date: 1-Jul-2013. https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijiscram.2013070104 521. Liu Y and Vitolo T. Graph Data Warehouse. Proceedings of the 2013 IEEE International Congress on Big Data. (433-434). https://doi.org/10.1109/BigData.Congress.2013.72 522. De Virgilio R, Maccioni A and Torlone R. Converting relational to graph databases. First International Workshop on Graph Data Management Experiences and Systems. (1-6). https://doi.org/10.1145/2484425.2484426 523. Libkin L, Reutter J and Vrgoč D. Trial for RDF. Proceedings of the 32nd ACM SIGMOD-SIGACT-SIGAI symposium on Principles of database systems. (201-212). https://doi.org/10.1145/2463664.2465226 524. Barceló Baeza P. Querying graph databases. Proceedings of the 32nd ACM SIGMOD-SIGACT-SIGAI symposium on Principles of database systems. (175-188). https://doi.org/10.1145/2463664.2465216 525. Barceló Baeza P, Romero M and Vardi M. Semantic acyclicity on graph databases. Proceedings of the 32nd ACM SIGMOD-SIGACT-SIGAI symposium on Principles of database systems. (237-248). https://doi.org/10.1145/2463664.2463671 526. Liptchinsky V, Satzger B, Zabolotnyi R and Dustdar S. Expressive languages for selecting groups from graph-structured data. Proceedings of the 22nd international conference on World Wide Web. (761-770). https://doi.org/10.1145/2488388.2488455 527. Shijie Zhang , Jiong Yang and Sun B. (2013). SuReQL: A subgraph match based relational model for large graphs 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW 2013). 10.1109/ICDEW.2013.6547452. 978-1-4673-5304-5. (212-215). http://ieeexplore.ieee.org/document/6547452/ 528. Ames S, Gokhale M and Maltzahn C. (2013). QMDS. International Journal of Parallel, Emergent and Distributed Systems. 28:2. (159-183). Online publication date: 1-Apr-2013. https://doi.org/10.1080/17445760.2012.658802 529. Libkin L, Martens W and Vrgoč D. Querying graph databases with XPath. Proceedings of the 16th International Conference on Database Theory. (129-140). https://doi.org/10.1145/2448496.2448513 530. Hellings J, Kuijpers B, Van den Bussche J and Zhang X. Walk logic as a framework for path query languages on graph databases. Proceedings of the 16th International Conference on Database Theory. (117-128). https://doi.org/10.1145/2448496.2448512 531. Barceló P, Reutter J and Libkin L. (2013). Parameterized regular expressions and their languages. Theoretical Computer Science. 474. (21-45). Online publication date: 1-Feb-2013. https://doi.org/10.1016/j.tcs.2012.12.036 532. Dąbrowski R, Timoszuk G and Stencel K. (2013). One Graph to Rule Them All Software Measurement and Management. Fundamenta Informaticae. 128:1-2. (47-63). Online publication date: 1-Jan-2013. /doi/10.5555/2594991.2594996 533. Sakr S and Al-Naymat G. An Overview of Graph Indexing and Querying Techniques. Bioinformatics. 10.4018/978-1-4666-3604-0.ch011. (222-239). http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-4666-3604-0.ch011 534. Farley T, Kiefer J, Lee P, Von Hoff D, Trent J, Colbourn C and Mousses S. (2013). The BioIntelligence Framework: a new computational platform for biomedical knowledge computing. Journal of the American Medical Informatics Association. 10.1136/amiajnl-2011-000646. 20:1. (128-133). Online publication date: 1-Jan-2013. https://academic.oup.com/jamia/article-lookup/doi/10.1136/amiajnl-2011-000646 535. Soussi R, Cuvelier E, Aufaure M, Louati A and Lechevallier Y. (2013). DB2SNA: An All-in-One Tool for Extraction and Aggregation of Underlying Social Networks from Relational Databases. The Influence of Technology on Social Network Analysis and Mining. 10.1007/978-3-7091-1346-2_23. (521-545). https://link.springer.com/10.1007/978-3-7091-1346-2_23 536. Barceló P, Fontaine G and Lin A. (2013). Expressive Path Queries on Graphs with Data. Logic for Programming, Artificial Intelligence, and Reasoning. 10.1007/978-3-642-45221-5_5. (71-85). http://link.springer.com/10.1007/978-3-642-45221-5_5 537. Dimitrov D, Singh L and Mann J. (2013). Comparison Queries for Uncertain Graphs. Database and Expert Systems Applications. 10.1007/978-3-642-40173-2_13. (124-140). http://link.springer.com/10.1007/978-3-642-40173-2_13 538. Kirrane S, Mileo A and Decker S. (2013). Applying DAC Principles to the RDF Graph Data Model. Security and Privacy Protection in Information Processing Systems. 10.1007/978-3-642-39218-4_6. (69-82). http://link.springer.com/10.1007/978-3-642-39218-4_6 539. Libkin L, Tan T and Vrgoč D. (2013). Regular Expressions with Binding over Data Words for Querying Graph Databases. Developments in Language Theory. 10.1007/978-3-642-38771-5_29. (325-337). http://link.springer.com/10.1007/978-3-642-38771-5_29 540. de Cesare S, Foy G and Partridge C. (2013). Re-engineering Data with 4D Ontologies and Graph Databases. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. 10.1007/978-3-642-38490-5_29. (304-316). http://link.springer.com/10.1007/978-3-642-38490-5_29 541. Pereira A and Appel A. (2013). Modeling and Storing Complex Network with Graph-Tree. New Trends in Databases and Information Systems. 10.1007/978-3-642-32518-2_29. (305-315). https://link.springer.com/10.1007/978-3-642-32518-2_29 542. Dąbrowski R. On architecture warehouses and software intelligence. Proceedings of the 4th international conference on Future Generation Information Technology. (251-262). https://doi.org/10.1007/978-3-642-35585-1_35 543. Hu B, Carvalho N, Laera L and Matsutsuka T. Towards big linked data. Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services. (167-176). https://doi.org/10.1145/2428736.2428764 544. Soussi R. SPIDER-graph. Proceedings of the 31st international conference on Conceptual Modeling. (543-552). https://doi.org/10.1007/978-3-642-34002-4_44 545. Hoang D, Tjoa A and Priebe T. (2012). Query-by-example approach towards on-demand multidimensional analysis. International Journal of Business Intelligence and Data Mining. 7:3. (137-151). Online publication date: 1-Oct-2012. https://doi.org/10.1504/IJBIDM.2012.049551 546. Zhang J, Liu H and Yu H. (2012). The motive for constructing a high level data model on cloud databases 2012 World Congress on Information and Communication Technologies (WICT). 10.1109/WICT.2012.6409257. 978-1-4673-4805-8. (1198-1203). http://ieeexplore.ieee.org/document/6409257/ 547. Indrawan-Santiago M. Database Research. Proceedings of the 2012 15th International Conference on Network-Based Information Systems. (45-51). https://doi.org/10.1109/NBiS.2012.95 548. Šipetić M. Design and implementation of a space model server for indoor location-based services. Proceedings of the 2012 ACM Conference on Ubiquitous Computing. (572-575). https://doi.org/10.1145/2370216.2370312 549. Jayathilake D, Sooriaarachchi C, Gunawardena T, Kulasuriya B and Dayaratne T. (2012). A study into the capabilities of NoSQL databases in handling a highly heterogeneous tree 2012 IEEE 6th International Conference on Information and Automation for Sustainability (ICIAfS). 10.1109/ICIAFS.2012.6419890. 978-1-4673-1975-1. (106-111). http://ieeexplore.ieee.org/document/6419890/ 550. Martínez-Bazan N, Águila-Lorente M, Muntés-Mulero V, Dominguez-Sal D, Gómez-Villamor S and Larriba-Pey J. Efficient graph management based on bitmap indices. Proceedings of the 16th International Database Engineering & Applications Sysmposium. (110-119). https://doi.org/10.1145/2351476.2351489 551. Barcelo P, Figueira D and Libkin L. Graph Logics with Rational Relations and the Generalized Intersection Problem. Proceedings of the 2012 27th Annual IEEE/ACM Symposium on Logic in Computer Science. (115-124). https://doi.org/10.1109/LICS.2012.23 552. Hadjali A, Mokhtari A and Pivert O. (2012). Expressing and processing complex preferences in route planning queries. Fuzzy Sets and Systems. 196. (82-104). Online publication date: 1-Jun-2012. https://doi.org/10.1016/j.fss.2012.01.006 553. Barricelli B, Valtolina S and Marzullo M. ArchMatrix. Proceedings of the International Working Conference on Advanced Visual Interfaces. (681-684). https://doi.org/10.1145/2254556.2254684 554. Losemann K. Foundations of regular expressions in XML schema languages and SPARQL. Proceedings of the on SIGMOD/PODS 2012 PhD Symposium. (39-44). https://doi.org/10.1145/2213598.2213609 555. Sheng C, Tao Y and Li J. (2012). Exact and approximate algorithms for the most connected vertex problem. ACM Transactions on Database Systems. 37:2. (1-39). Online publication date: 1-May-2012. https://doi.org/10.1145/2188349.2188354 556. Wood P. (2012). Query languages for graph databases. ACM SIGMOD Record. 41:1. (50-60). Online publication date: 25-Apr-2012. https://doi.org/10.1145/2206869.2206879 557. Ritter D. From network mining to large scale business networks. Proceedings of the 21st International Conference on World Wide Web. (989-996). https://doi.org/10.1145/2187980.2188233 558. Cheng R, Hong J, Kyrola A, Miao Y, Weng X, Wu M, Yang F, Zhou L, Zhao F and Chen E. Kineograph. Proceedings of the 7th ACM european conference on Computer Systems. (85-98). https://doi.org/10.1145/2168836.2168846 559. Low Y, Bickson D, Gonzalez J, Guestrin C, Kyrola A and Hellerstein J. (2012). Distributed GraphLab. Proceedings of the VLDB Endowment. 5:8. (716-727). Online publication date: 1-Apr-2012. https://doi.org/10.14778/2212351.2212354 560. Angles R. A Comparison of Current Graph Database Models. Proceedings of the 2012 IEEE 28th International Conference on Data Engineering Workshops. (171-177). https://doi.org/10.1109/ICDEW.2012.31 561. Libkin L and Vrgoč D. Regular path queries on graphs with data. Proceedings of the 15th International Conference on Database Theory. (74-85). https://doi.org/10.1145/2274576.2274585 562. Libkin L and Vrgoč D. Regular expressions for data words. Proceedings of the 18th international conference on Logic for Programming, Artificial Intelligence, and Reasoning. (274-288). https://doi.org/10.1007/978-3-642-28717-6_22 563. Aspinall D, Denney E and Lüth C. Querying proofs. Proceedings of the 18th international conference on Logic for Programming, Artificial Intelligence, and Reasoning. (92-106). https://doi.org/10.1007/978-3-642-28717-6_10 564. Fletcher G, Gyssens M, Leinders D, Van den Bussche J, Van Gucht D, Vansummeren S and Wu Y. The impact of transitive closure on the boolean expressiveness of navigational query languages on graphs. Proceedings of the 7th international conference on Foundations of Information and Knowledge Systems. (124-143). https://doi.org/10.1007/978-3-642-28472-4_8 565. Dries A, Nijssen S and De Raedt L. BiQL. Bisociative Knowledge Discovery. (147-165). /doi/10.5555/2363300.2363314 566. Alkhateeb F and Euzenat J. Querying RDF Data. Graph Data Management. 10.4018/978-1-61350-053-8.ch015. (335-353). http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-61350-053-8.ch015 567. Sakr S and Al-Naymat G. An Overview of Graph Indexing and Querying Techniques. Graph Data Management. 10.4018/978-1-61350-053-8.ch004. (71-88). http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-61350-053-8.ch004 568. Srinivasa S. Data, Storage and Index Models for Graph Databases. Graph Data Management. 10.4018/978-1-61350-053-8.ch003. (47-70). http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-61350-053-8.ch003 569. Rodriguez M and Neubauer P. The Graph Traversal Pattern. Graph Data Management. 10.4018/978-1-61350-053-8.ch002. (29-46). http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-61350-053-8.ch002 570. Ritter D. (2012). The Business Graph Protocol. Information and Software Technologies. 10.1007/978-3-642-33308-8_19. (226-240). http://link.springer.com/10.1007/978-3-642-33308-8_19 571. Zhao Z, Ahn G, Hu H and Mahi D. (2012). SocialImpact: Systematic Analysis of Underground Social Dynamics. Computer Security – ESORICS 2012. 10.1007/978-3-642-33167-1_50. (877-894). http://link.springer.com/10.1007/978-3-642-33167-1_50 572. Dries A, Nijssen S and De Raedt L. (2012). BiQL: A Query Language for Analyzing Information Networks. Bisociative Knowledge Discovery. 10.1007/978-3-642-31830-6_11. (147-165). http://link.springer.com/10.1007/978-3-642-31830-6_11 573. Luo Y, Picalausa F, Fletcher G, Hidders J and Vansummeren S. (2012). Storing and Indexing Massive RDF Datasets. Semantic Search over the Web. 10.1007/978-3-642-25008-8_2. (31-60). http://link.springer.com/10.1007/978-3-642-25008-8_2 574. Hoang D, Priebe T and Tjoa A. Hypergraph-based multidimensional data modeling towards on-demand business analysis. Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services. (36-43). https://doi.org/10.1145/2095536.2095545 575. Ivanova V and Stromback L. Creating Infrastructure for Tool-Independent Querying and Exploration of Scientific Workflows. Proceedings of the 2011 IEEE Seventh International Conference on eScience. (287-294). https://doi.org/10.1109/eScience.2011.47 576. Sahoo S, Nguyen V, Bodenreider O, Parikh P, Minning T and Sheth A. (2011). A unified framework for managing provenance information in translational research. BMC Bioinformatics. 10.1186/1471-2105-12-461. 12:1. Online publication date: 1-Dec-2011. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-461 577. (2011). References. Data Management of Protein Interaction Networks. 10.1002/9781118103746.refs. (157-176). Online publication date: 4-Nov-2011. https://onlinelibrary.wiley.com/doi/10.1002/9781118103746.refs 578. YU G, GU Y, BAO Y and WANG Z. (2011). Large Scale Graph Data Processing on Cloud Computing Environments. Chinese Journal of Computers. 10.3724/SP.J.1016.2011.01753. 34:10. (1753-1767). Online publication date: 28-Oct-2011. http://pub.chinasciencejournal.com/article/getArticleRedirect.action?doiCode=10.3724/SP.J.1016.2011.01753 579. Tausch N, Philippsen M and Adersberger J. A statically typed query language for property graphs. Proceedings of the 15th Symposium on International Database Engineering & Applications. (219-225). https://doi.org/10.1145/2076623.2076653 580. Dąbrowski R, Stencel K and Timoszuk G. Software is a directed multigraph. Proceedings of the 5th European conference on Software architecture. (360-369). /doi/10.5555/2041790.2041838 581. Beheshti S, Benatallah B, Motahari-Nezhad H and Sakr S. A query language for analyzing business processes execution. Proceedings of the 9th international conference on Business process management. (281-297). /doi/10.5555/2040283.2040308 582. Gutierrez C. Modeling the web of data. Proceedings of the 7th international conference on Reasoning web: semantic technologies for the web of data. (416-444). /doi/10.5555/2033313.2033321 583. Ames S, Gokhale M and Maltzahn C. QMDS. Proceedings of the 2011 IEEE Sixth International Conference on Networking, Architecture, and Storage. (268-277). https://doi.org/10.1109/NAS.2011.33 584. Inaba K, Hidaka S, Hu Z, Kato H and Nakano K. Graph-transformation verification using monadic second-order logic. Proceedings of the 13th international ACM SIGPLAN symposium on Principles and practices of declarative programming. (17-28). https://doi.org/10.1145/2003476.2003482 585. Braunschweig K, Thiele M and Lehner W. A flexible graph-based data model supporting incremental schema design and evolution. Proceedings of the 11th international conference on Current Trends in Web Engineering. (302-306). https://doi.org/10.1007/978-3-642-27997-3_29 586. Arenas M and Pérez J. Querying semantic web data with SPARQL. Proceedings of the thirtieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems. (305-316). https://doi.org/10.1145/1989284.1989312 587. Barceló P, Libkin L and Reutter J. Querying graph patterns. Proceedings of the thirtieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems. (199-210). https://doi.org/10.1145/1989284.1989307 588. Takada S. Finding web services via BPEL fragment search. Proceedings of the 3rd International Workshop on Search-Driven Development: Users, Infrastructure, Tools, and Evaluation. (9-12). https://doi.org/10.1145/1985429.1985432 589. Prat-Pérez A, Dominguez-Sal D and Larriba-Pey J. Social based layouts for the increase of locality in graph operations. Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I. (558-569). /doi/10.5555/1997305.1997356 590. Martinez-Bazan N, Gomez-Villamor S and Escale-Claveras F. DEX. Proceedings of the 2011 IEEE 27th International Conference on Data Engineering Workshops. (124-127). https://doi.org/10.1109/ICDEW.2011.5767616 591. Fletcher G, Gyssens M, Leinders D, Van den Bussche J, Van Gucht D, Vansummeren S and Wu Y. Relative expressive power of navigational querying on graphs. Proceedings of the 14th International Conference on Database Theory. (197-207). https://doi.org/10.1145/1938551.1938578 592. Sakr S and Al-Naymat G. Querying Graph Databases. Advanced Database Query Systems. 10.4018/978-1-60960-475-2.ch013. (304-322). http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-60960-475-2.ch013 593. Dąbrowski R, Stencel K and Timoszuk G. (2011). Software Is a Directed Multigraph. Software Architecture. 10.1007/978-3-642-23798-0_38. (360-369). http://link.springer.com/10.1007/978-3-642-23798-0_38 594. Beheshti S, Benatallah B, Motahari-Nezhad H and Sakr S. (2011). A Query Language for Analyzing Business Processes Execution. Business Process Management. 10.1007/978-3-642-23059-2_22. (281-297). http://link.springer.com/10.1007/978-3-642-23059-2_22 595. Gutierrez C. (2011). Modeling the Web of Data (Introductory Overview). Reasoning Web. Semantic Technologies for the Web of Data. 10.1007/978-3-642-23032-5_8. (416-444). http://link.springer.com/10.1007/978-3-642-23032-5_8 596. Plantikow S and Jorra M. (2011). Latency-Optimal Walks in Replicated and Partitioned Graphs. Database Systems for Adanced Applications. 10.1007/978-3-642-20244-5_3. (14-27). http://link.springer.com/10.1007/978-3-642-20244-5_3 597. Prat-Pérez A, Dominguez-Sal D and Larriba-Pey J. (2011). Social Based Layouts for the Increase of Locality in Graph Operations. Database Systems for Advanced Applications. 10.1007/978-3-642-20149-3_40. (558-569). http://link.springer.com/10.1007/978-3-642-20149-3_40 598. Soussi R, Aufaure M and Baazaoui H. (2011). Graph Database for Collaborative Communities. Community-Built Databases. 10.1007/978-3-642-19047-6_9. (205-234). https://link.springer.com/10.1007/978-3-642-19047-6_9 599. Dominguez-Sal D, Martinez-Bazan N, Muntes-Mulero V, Baleta P and Larriba-Pey J. (2011). A Discussion on the Design of Graph Database Benchmarks. Performance Evaluation, Measurement and Characterization of Complex Systems. 10.1007/978-3-642-18206-8_3. (25-40). http://link.springer.com/10.1007/978-3-642-18206-8_3 600. Cannataro M, Guzzi P and Veltri P. (2010). Protein-to-protein interactions. ACM Computing Surveys. 43:1. (1-36). Online publication date: 1-Nov-2010. https://doi.org/10.1145/1824795.1824796 601. Pérez J, Arenas M and Gutierrez C. (2010). nSPARQL. Web Semantics: Science, Services and Agents on the World Wide Web. 8:4. (255-270). Online publication date: 1-Nov-2010. https://doi.org/10.1016/j.websem.2010.01.002 602. Dries A and Nijssen S. Analyzing graph databases by aggregate queries. Proceedings of the Eighth Workshop on Mining and Learning with Graphs. (37-45). https://doi.org/10.1145/1830252.1830258 603. Iordanov B. HyperGraphDB. Proceedings of the 2010 international conference on Web-age information management. (25-36). /doi/10.5555/1927585.1927589 604. Saber M, Aref M and Gharib T. (2010). An efficient filtering technique for super-graph query processing 2010 Fifth International Conference on Digital Information Management (ICDIM). 10.1109/ICDIM.2010.5664712. 978-1-4244-7572-8. (174-181). http://ieeexplore.ieee.org/document/5664712/ 605. Sakr S and Al‐Naymat G. (2010). Graph indexing and querying: a review. International Journal of Web Information Systems. 10.1108/17440081011053104. 6:2. (101-120). Online publication date: 22-Jun-2010. https://www.emerald.com/insight/content/doi/10.1108/17440081011053104/full/html 606. Tao Y, Sheng C and Li J. Finding maximum degrees in hidden bipartite graphs. Proceedings of the 2010 ACM SIGMOD International Conference on Management of data. (891-902). https://doi.org/10.1145/1807167.1807263 607. Vicknair C, Macias M, Zhao Z, Nan X, Chen Y and Wilkins D. A comparison of a graph database and a relational database. Proceedings of the 48th Annual Southeast Regional Conference. (1-6). https://doi.org/10.1145/1900008.1900067 608. Soussi R, Aufaure M and Baazaoui H. Towards Social Network Extraction Using a Graph Database. Proceedings of the 2010 Second International Conference on Advances in Databases, Knowledge, and Data Applications. (28-34). https://doi.org/10.1109/DBKDA.2010.19 609. Ciglan M and Nørvåg K. SGDB. Proceedings of the 15th international conference on Database systems for advanced applications. (45-56). /doi/10.5555/1880853.1880859 610. Terje Bjørke J, Nilsen S and Varga M. (2010). Visualization of network structure by the application of hypernodes. International Journal of Approximate Reasoning. 51:3. (275-293). Online publication date: 1-Feb-2010. https://doi.org/10.1016/j.ijar.2009.09.003 611. Laha A. On the issues of building information warehouses. Proceedings of the Third Annual ACM Bangalore Conference. (1-8). https://doi.org/10.1145/1754288.1754290 612. Grefenstette G and Wilber L. (2010). Search-Based Applications: At the Confluence of Search and Database Technologies. Synthesis Lectures on Information Concepts, Retrieval, and Services. 10.2200/S00320ED1V01Y201012ICR017. 2:1. (1-141). Online publication date: 1-Jan-2010. http://www.morganclaypool.com/doi/abs/10.2200/S00320ED1V01Y201012ICR017 613. Iordanov B. (2010). HyperGraphDB: A Generalized Graph Database. Web-Age Information Management. 10.1007/978-3-642-16720-1_3. (25-36). http://link.springer.com/10.1007/978-3-642-16720-1_3 614. Ciglan M and Nørvåg K. (2010). SGDB – Simple Graph Database Optimized for Activation Spreading Computation. Database Systems for Advanced Applications. 10.1007/978-3-642-14589-6_5. (45-56). http://link.springer.com/10.1007/978-3-642-14589-6_5 615. Fletcher G and Beck P. Scalable indexing of RDF graphs for efficient join processing. Proceedings of the 18th ACM conference on Information and knowledge management. (1513-1516). https://doi.org/10.1145/1645953.1646159 616. Dries A, Nijssen S and De Raedt L. A query language for analyzing networks. Proceedings of the 18th ACM conference on Information and knowledge management. (485-494). https://doi.org/10.1145/1645953.1646016 617. Benazzouz Y, Beaune P, Ramparany F and Chotard L. (2009). Context data-driven approach for ubiquitous computing applications 2009 Fourth International Conference on Digital Information Management (ICDIM). 10.1109/ICDIM.2009.5356771. 978-1-4244-4253-9. (1-6). http://ieeexplore.ieee.org/document/5356771/ 618. Arenas M, Gutierrez C and Pérez J. Foundations of RDF Databases. Reasoning Web. Semantic Technologies for Information Systems. (158-204). https://doi.org/10.1007/978-3-642-03754-2_4 619. Hoareau C and Satoh I. From model checking to data management in pervasive computing. Proceedings of the 2009 international conference on Pervasive services. (41-48). https://doi.org/10.1145/1568199.1568206 620. Roussopoulos N and Karagiannis D. Conceptual Modeling. Conceptual Modeling: Foundations and Applications. (139-152). https://doi.org/10.1007/978-3-642-02463-4_9 621. Jin R, Xiang Y, Ruan N and Fuhry D. 3-HOP. Proceedings of the 2009 ACM SIGMOD International Conference on Management of data. (813-826). https://doi.org/10.1145/1559845.1559930 622. San Martín M and Gutierrez C. Representing, Querying and Transforming Social Networks with RDF/SPARQL. Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications. (293-307). https://doi.org/10.1007/978-3-642-02121-3_24 623. Hoareau C and Satoh I. (2009). Modeling and Processing Information for Context-Aware Computing: A Survey. New Generation Computing. 27:3. (177-196). Online publication date: 1-May-2009. https://doi.org/10.1007/s00354-009-0060-5 624. Ronen R and Shmueli O. SoQL. Proceedings of the 2009 IEEE International Conference on Data Engineering. (1595-1602). https://doi.org/10.1109/ICDE.2009.172 625. Gupta A. (2009). Graph Data Management in Scientific Applications. Encyclopedia of Database Systems. 10.1007/978-0-387-39940-9_1298. (1261-1263). http://link.springer.com/10.1007/978-0-387-39940-9_1298 626. Pérez J, Arenas M and Gutierrez C. nSPARQL. Proceedings of the 7th International Conference on The Semantic Web. (66-81). https://doi.org/10.1007/978-3-540-88564-1_5 627. Lobo J and Pappas V. C2. Proceedings of the 2008 IEEE Workshop on Policies for Distributed Systems and Networks. (29-36). https://doi.org/10.1109/POLICY.2008.45 628. Arenas M, Gutierrez C and Pérez J. An Extension of SPARQL for RDFS. Semantic Web, Ontologies and Databases. (1-20). https://doi.org/10.1007/978-3-540-70960-2_1 629. Yang B and O'Hallaron D. (1997). Parallel breadth-first BDD construction. ACM SIGPLAN Notices. 32:7. (145-156). Online publication date: 1-Jul-1997. https://doi.org/10.1145/263767.263784 630. Dahlbom B and Mathiassen L. (1997). The future of our profession. Communications of the ACM. 40:6. (80-89). Online publication date: 1-Jun-1997. https://doi.org/10.1145/255656.255706 631. Parsons J and Wand Y. (1997). Choosing classes in conceptual modeling. Communications of the ACM. 40:6. (63-69). Online publication date: 1-Jun-1997. https://doi.org/10.1145/255656.255700 632. Perez J, Arenas M and Gutiirrez C. nSPARQL: A Navigational Language for RDF. SSRN Electronic Journal. 10.2139/ssrn.3199487. https://www.ssrn.com/abstract=3199487 633. Poulovassilis A, Selmer P and Wood P. Approximation and Relaxation of Semantic Web Path Queries. SSRN Electronic Journal. 10.2139/ssrn.3199265. https://www.ssrn.com/abstract=3199265 Show All Cited By INDEX TERMS 1. Survey of graph database models 1. Computer systems organization 1. Architectures 1. Other architectures 1. Heterogeneous (hybrid) systems 2. Information systems 1. Data management systems 1. Database design and models 1. Data model extensions 2. Database management system engines 3. Information integration 1. Extraction, transformation and loading 4. Query languages 2. Information systems applications 3. Mathematics of computing 1. Discrete mathematics 1. Graph theory 1. Graph enumeration 2. Hypergraphs 3. Network flows 4. Paths and connectivity problems 4. Theory of computation 1. Design and analysis of algorithms 1. Graph algorithms analysis 1. Network flows 2. Theory and algorithms for application domains 1. Database theory 1. Database query languages (principles) RECOMMENDATIONS * DEMYSTIFYING GRAPH DATABASES: ANALYSIS AND TAXONOMY OF DATA ORGANIZATION, SYSTEM DESIGNS, AND GRAPH QUERIES Numerous irregular graph datasets, for example social networks or web graphs, may contain even trillions of edges. Often, their structure changes over time and they have domain-specific rich data associated with vertices and edges. Graph database systems ... Read More * A GRAPH-ORIENTED OBJECT DATABASE MODEL A graph-oriented object database model (GOOD) is introduced as a theoretical basis for database systems in which manipulation as well as conceptual representation of data is transparently graph-based. In the GOOD model, the scheme as well as the ... Read More * IBM RELATIONAL DATABASE SYSTEMS: THE EARLY YEARS The relational data model, proposed by E.F. Codd in 1970, inspired several research projects at IBM and elsewhere. Among these was System R, which demonstrated the commercial viability of relational database systems. This article describes the research ... Read More COMMENTS Please enable JavaScript to view thecomments powered by Disqus. LOGIN OPTIONS Check if you have access through your login credentials or your institution to get full access on this article. Sign in FULL ACCESS Get this Article * Information * Contributors * PUBLISHED IN ACM Computing Surveys Volume 40, Issue 1 February 2008 172 pages ISSN:0360-0300 EISSN:1557-7341 DOI:10.1145/1322432 Issue’s Table of Contents Copyright © 2008 ACM Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. 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Request Permissions CHECK FOR UPDATES AUTHOR TAGS * Database systems * database models * graph database models * graph databases * graph integrity constraints * graph query languages QUALIFIERS * research-article * Research * Refereed CONFERENCE FUNDING SOURCES * Millenium Nucleus, Center for Wed Research * OTHER METRICS View Article Metrics * Bibliometrics * Citations633 * ARTICLE METRICS * 633 Total Citations View Citations * 17,463 Total Downloads * Downloads (Last 12 months)882 * Downloads (Last 6 weeks)107 OTHER METRICS View Author Metrics * CITED BY View all PDF FORMAT View or Download as a PDF file. PDF EREADER View online with eReader. eReader DIGITAL EDITION View this article in digital edition. View Digital Edition 1. Abiteboul, S. 1997. Querying semi-structured data. In Proceedings of the 6th International Conference on Database Theory (ICDT). LNCS, vol. 1186. Springer, 1--18. Google ScholarDigital Library 2. Abiteboul, S. and Hull, R. 1984. IFO: A formal semantic database model. In Proceedings of the 3th Symposium on Principles of Database Systems (PODS). ACM Press, 119--132. Google ScholarDigital Library 3. Abiteboul, S., Quass, D., McHugh, J., Widom, J., and Wiener, J. L. 1997. The Lorel query language for semistructured data. Int. J. Dig. Libr. 1, 1, 68--88.Google ScholarCross Ref 4. Abiteboul, S. and Vianu, V. 1997. Queries and computation on the Web. In Proceedings of the 6th International Conference on Database Theory (ICDT). LNCS, vol. 1186. Springer, 262--275. Google ScholarDigital Library 5. Agrawal, R. and Jagadish, H. V. 1988. Efficient search in very large databases. In Proceedings of the 14th International Conference on Very Large Data Bases (VLDB). Morgan Kaufmann, 407--418. Google ScholarDigital Library 6. Agrawal, R. and Jagadish, H. V. 1989. Materialization and incremental update of path information. In Proceedings of the 5th International Conference on Data Engineering (ICDE). IEEE Computer Society, 374--383. Google ScholarDigital Library 7. Agrawal, R. and Jagadish, H. V. 1994. Algorithms for searching massive graphs. IEEE Trans. Knowl. Data Eng. 6, 2, 225--238. Google ScholarDigital Library 8. Albert, R. and Barabási, A.-L. 2002. Statistical mechanics of complex networks. Rev. Mod. Phy. 74, 47.Google ScholarCross Ref 9. Alechina, N., Demri, S., and de Rijke, M. 2003. A modal perspective on path constraints. J. Logic Computation 13, 6, 939--956.Google ScholarCross Ref 10. Amann, B. and Scholl, M. 1992. Gram: A Graph Data Model and Query Language. In European Conference on Hypertext Technology (ECHT). ACM, 201--211. Google ScholarDigital Library 11. Andries, M. and Engels, G. 1993. A hybrid query language for an extended entity-relationship model. Tech. Rep. TR 93-15, Institute of Advanced Computer Science, Universiteit Leiden. May.Google Scholar 12. Andries, M., Gemis, M., Paredaens, J., Thyssens, I., and den Bussche, J. V. 1992. Concepts for graph-oriented object manipulation. In Proceedings of the 3rd International Conference on Extending Database Technology (EDBT). LNCS, vol. 580. Springer, 21--38. Google ScholarDigital Library 13. Angles, R. and Gutierrez, C. 2005. Querying RDF data from a graph database perspective. In Proceedings of the 2nd European Semantic Web Conference (ESWC). Number 3532 in LNCS. 346--360. Google ScholarDigital Library 14. Aufaure-Portier, M.-A. and Trépied, C. 1976. A survey of query languages for geographic information systems. In Proceedings of the 3rd International Workshop on Interfaces to Databases. 431--438.Google Scholar 15. Azmoodeh, M. and Du, H. 1988. GQL, A graphical query language for semantic databases. In Proceedings of the 4th International Conference on Scientific and Statistical Database Management (SSDBM). LNCS, vol. 339. Springer, 259--277. Google ScholarDigital Library 16. Beeri, C. 1988. Data models and languages for databases. In Proceedings of the 2nd International Conference on Database Theory (ICDT). LNCS, vol. 326. Springer, 19--40. Google ScholarDigital Library 17. Benkö, G., Flamm, C., and Stadler, P. F. 2003. A graph-based toy model of chemistry. J. Chem. Inform. Computer Science (JCISD) 43, 1 (Jan), 1085--1093.Google Scholar 18. Berge, C. 1973. Graphs and Hypergraphs. North-Holland, Amsterdam. Google ScholarDigital Library 19. Brandes, U. 2005. Network Analysis. Number 3418 in LNCS. Springer-Verlag.Google Scholar 20. Bray, T., Paoli, J., and Sperberg-McQueen, C. M. 1998. Extensible Markup Language (XML) 1.0, W3C Recommendation 10, (February). http://www.w3.org/TR/1998/REC-xml-19980210. Google ScholarDigital Library 21. Broder, A., Kumar, R., Maghoul, F., Raghavan, P., Rajagopalan, S., Stata, R., Tomkins, A., and Wiener, J. 2000. Graph structure in the Web. In Proceedings of the 9th International World Wide Web conference on Computer Networks: The International Journal of Computer and Telecommunications Networking. North-Holland Publishing Co., 309--320. Google ScholarDigital Library 22. Buneman, P. 1997. Semistructured data. In Proceedings of the 16th Symposium on Principles of Database Systems (PODS). ACM Press, 117--121. Google ScholarDigital Library 23. Buneman, P., Davidson, S., Hillebrand, G., and Suciu, D. 1996. A query language and optimization techniques for unstructured data. SIGMOD Record. 25, 2, 505--516. Google ScholarDigital Library 24. Buneman, P., Fan, W., and Weinstein, S. 1998. Path constraints in semistructured and structured databases. In Proceedings of the 17th Symposium on Principles of Database Systems (PODS). ACM Press, 129-- 138. Google ScholarDigital Library 25. Cardelli, L., Gardner, P., and Ghelli, G. 2002. A spatial logic for querying graphs. In Proceedings of the 29th International Colloquium on Automata, Languages, and Programming (ICALP). LNCS. Springer, 597--610. Google ScholarDigital Library 26. Chandra, A. K. 1988. Theory of database queries. In Proceedings of the 7th Symposium on Principles of Database Systems (PODS). ACM Press, 1--9. Google ScholarDigital Library 27. Chen, P. P.-S. 1976. The entity-relationship model---toward a unified view of data. ACM Trans. Database Syst. 1, 1, 9--36. Google ScholarDigital Library 28. Chomicki, J. 1994. Temporal query languages: a survey. In Proceedings of the First International Conference on Temporal Logic (ICTL). Springer-Verlag, 506--534. Google ScholarDigital Library 29. Codd, E. F. 1970. A relational model of data for large shared data banks. Commun. ACM 13, 6, 377-- 387. Google ScholarDigital Library 30. Codd, E. F. 1980. Data models in database management. In Proceedings of the 1980 Workshop on Data abstraction, Databases, and Conceptual Modeling. ACM Press, 112--114. Google ScholarDigital Library 31. Codd, E. F. 1983. A relational model of data for large shared data banks. Commun. ACM 26, 1, 64--69. Google ScholarDigital Library 32. Conklin, J. 1987. Hypertext: An introduction and survey. IEEE Comput. 20, 9, 17--41. Google ScholarDigital Library 33. Consens, M. and Mendelzon, A. 1993. Hy+: A hygraph-based query and visualization system. SIGMOD Record 22, 2, 511--516. Google ScholarDigital Library 34. Consens, M. P. and Mendelzon, A. O. 1989. Expressing structural hypertext queries in graphlog. In Proceedings of the 2th Conference on Hypertext. ACM Press, 269--292. Google ScholarDigital Library 35. Cruz, I. F., Mendelzon, A. O., and Wood, P. T. 1987. A graphical query language supporting recursion. In Proceedings of the Association for Computing Machinery Special Interest Group on Management of Data. ACM Press, 323--330. Google ScholarDigital Library 36. Cruz, I. F., Mendelzon, A. O., and Wood, P. T. 1989. G+: recursive queries without recursion. In Proceedings of the 2th International Conference on Expert Database Systems (EDS). Addison-Wesley, 645-- 666.Google Scholar 37. Date, C. J. 1981. Referential integrity. In Proceedings of the 7th International Conference on Very Large Data Bases (VLDB). IEEE Computer Society, 2--12. Google ScholarDigital Library 38. de S. Price, D. J. 1965. Networks of scientific papers. Science 149, 510--515.Google ScholarCross Ref 39. Deng, Y. and Chang, S.-K. 1990. A G-Net model for knowledge representation and reasoning. IEEE Trans. Knowl. Data Eng. 2, 3 (Dec), 295--310. Google ScholarDigital Library 40. Deville, Y., Gilbert, D., van Helden, J., and Wodak, S. J. 2003. An overview of data models for the analysis of biochemical pathways. In Proceedings of the First International Workshop on Computational Methods in Systems Biology. Springer-Verlag, 174. Google ScholarDigital Library 41. Dorogovtsev, S. N. and Mendes, J. F. F. 2003. Evolution of Networks---From Biological Nets to the Internet and WWW. Oxford University Press. Google ScholarDigital Library 42. Fernández, M., Florescu, D., Kang, J., Levy, A., and Suciu, D. 1998. Catching the boat with strudel: experiences with a Web-site management system. In Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data. ACM Press, 414--425. Google ScholarDigital Library 43. Flesca, S. and Greco, S. 1999. Partially ordered regular languages for graph queries. In Proceedings of the 26th International Colloquium on Automata, Languages and Programming (ICALP). LNCS, vol. 1644. Springer, 321--330. Google ScholarDigital Library 44. Flesca, S. and Greco, S. 2000. Querying graph databases. In Proceedings of the 7th International Conference on Extending Database Technology---Advances in Database Technology (EDBT). LNCS, vol. 1777. Springer, 510--524. Google ScholarDigital Library 45. Florescu, D., Levy, A., and Mendelzon, A. O. 1998. Database techniques for the World-Wide Web: A survey. SIGMOD Record 27, 3, 59--74. Google ScholarDigital Library 46. Fry, J. P. and Sibley, E. H. 1976. Evolution of data-base management systems. ACM Comput. Surv. 8, 1. Google ScholarDigital Library 47. Furche, T., Linse, B., Bry, F., Plexousakis, D., and Gottlob, G. 2006. RDF querying: language constructs and evaluation methods compared. In Reasoning Web. Number 4126 in LNCS. 1--52.Google Scholar 48. Gemis, M. and Paredaens, J. 1993. An object-oriented pattern matching language. In Proceedings of the First JSSST International Symposium on Object Technologies for Advanced Software. Springer-Verlag, 339--355. Google ScholarDigital Library 49. Gemis, M., Paredaens, J., Thyssens, I., and den Bussche, J. V. 1993. GOOD: A graph-oriented object database system. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data. ACM Press, 505--510. Google ScholarDigital Library 50. Giugno, R. and Shasha, D. 2002. GraphGrep: A fast and universal method for querying graphs. In Proceedings of the IEEE International Conference in Pattern recognition (ICPR).Google Scholar 51. Graves, M. Graph data models for genomics. http://www.xweave.com/people/in graaves/pubs.Google Scholar 52. Graves, M. 1993. Theories and tools for designing application-specific knowledge base data models. Ph.D. dissertation, University of Michigan. Google ScholarDigital Library 53. Graves, M., Bergeman, E. R., and Lawrence, C. B. 1994. Querying a genome database using graphs. In Proceedings of the 3th International Conference on Bioinformatics and Genome Research.Google Scholar 54. Graves, M., Bergeman, E. R., and Lawrence, C. B. 1995a. A graph-theoretic data model for genome mapping databases. In Proceedings of the 28th Hawaii International Conference on System Sciences (HICSS). IEEE Computer Society, 32. Google ScholarDigital Library 55. Graves, M., Bergeman, E. R., and Lawrence, C. B. 1995b. Graph database systems for genomics. IEEE Eng. Medicine Biol. Special issue on Managing Data for the Human Genome Project 11, 6.Google Scholar 56. Griffith, R. L. 1982. Three principles of representation for semantic networks. ACM Trans. Database Syst. 7, 3, 417--442. Google ScholarDigital Library 57. Guha, R.V., Lassila, O., Miller, E., and Brickley, D. 1998. Enabling inferencing. The Query Languages Workshop (QL).Google Scholar 58. Gutiérrez, A., Pucheral, P., Steffen, H., and Thévenin, J.-M. 1994. Database graph views: A practical model to manage persistent graphs. In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB). Morgan Kaufmann, 391--402. Google ScholarDigital Library 59. Güting, R. H. 1994. GraphDB: modeling and querying graphs in databases. In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB). Morgan Kaufmann, 297--308. Google ScholarDigital Library 60. Gyssens, M., Paredaens, J., den Bussche, J. V., and Gucht, D. V. 1990a. A graph-oriented object database model. In Proceedings of the 9th Symposium on Principles of Database Systems (PODS). ACM Press, 417--424. Google ScholarDigital Library 61. Gyssens, M., Paredaens, J., den Bussche, J. V., and Gucht, D. V. 1991. A graph-oriented object database model. Tech. Rep. 91-27, University of Antwerp (UIA), Belgium. (March).Google Scholar 62. Gyssens, M., Paredaens, J., and Gucht, D. V. 1990b. A graph-oriented object model for database end-user interfaces. In Proceedings of the 1990 ACM SIGMOD International Conference on Management of Data. ACM Press, 24--33. Google ScholarDigital Library 63. Hammer, J. and Schneider, M. 2004. The GenAlg project: developing a new integrating data model, language, and tool for managing and querying genomic information. SIGMOD Record 33, 2, 45--50. Google ScholarDigital Library 64. Hammer, M. and McLeod, D. 1978. The semantic data model: a modelling mechanism for data base applications. In Proceedings of the 1978 ACM SIGMOD International Conference on Management of Data. ACM, 26--36. Google ScholarDigital Library 65. Hanneman, R. A. 2001. Introduction to social network methods. Tech. Rep., Department of Sociology, University of California, Riverside.Google Scholar 66. Hayes, J. and Gutierrez, C. 2004. Bipartite graphs as intermediate model for RDF. In Proceedings of the 3th International Semantic Web Conference (ISWC). Number 3298 in LNCS. Springer-Verlag, 47--61.Google Scholar 67. Heuer, A. and Scholl, M. H. 1991. Principles of object-oriented query languages. In Datenbanksysteme in Büro, Technik und Wissenschaft (BTW). Informatik-Fachberichte, vol. 270. Springer, 178--197.Google Scholar 68. Hidders, J. 2001. A graph-based update language for object-oriented data models. Ph.D. dissertation, Technische Universiteit Eindhoven.Google Scholar 69. Hidders, J. 2002. Typing graph-manipulation operations. In Proceedings of the 9th International Conference on Database Theory (ICDT). Springer-Verlag, 394--409. Google ScholarDigital Library 70. Hidders, J. and Paredaens, J. 1993. GOAL, A graph-based object and association language. Advances in Database Systems: Implementations and Applications, CISM, 247--265.Google Scholar 71. Hull, R. and King, R. 1987. Semantic database modeling: Survey, applications, and research issues. ACM Comput. Surv. 19, 3, 201--260. Google ScholarDigital Library 72. ISO. 1999. International Standard ISO/IEC 13250 Topic Maps.Google Scholar 73. Jagadish, H. V. and Olken, F. 2003. Data management for the biosciences: report of the NLM Workshop on Data Management for Molecular and Cell Biology. Tech. Rep. LBNL-52767, National Library of Medicine.Google Scholar 74. Kerschberg, L., Klug, A. C., and Tsichritzis, D. 1976. A taxonomy of data models. In Proceedings of Systems for Large Data Bases (VLDB). North Holland and IFIP, 43--64. Google ScholarDigital Library 75. Kiesel, N., Schurr, A., and Westfechtel, B. 1996. GRAS: A graph-oriented software engineering database system. In IPSEN Book. 397--425. Google ScholarDigital Library 76. Kifer, M., Kim, W., and Sagiv, Y. 1992. Querying object-oriented databases. In Proceedings of the 1992 ACM SIGMOD International Conference on Management of Data. ACM Press, 393--402. Google ScholarDigital Library 77. Kim, W. 1990. Object-oriented databases: definition and research directions. IEEE Trans. Knowl. Data Eng. 2, 3, 327--341. Google ScholarDigital Library 78. Klyne, G. and Carroll, J. 2004. Resource description framework (RDF) concepts and abstract syntax. http://www.w3.org/TR/2004/REC-rdf-concepts-20040210/.Google Scholar 79. Kumar, R., Raghavan, P., Rajagopalan, S., Sivakumar, D., Tomkins, A., and Upfal, E. 2000. The Web as a graph. In Proceedings of the 19th Symposium on Principles of Database Systems (PODS). ACM Press, 1--10. Google ScholarDigital Library 80. Kunii, H. S. 1987. DBMS with graph data model for knowledge handling. In Proceedings of the 1987 Fall Joint Computer Conference on Exploring technology: Today and Tomorrow. IEEE Computer Society Press, 138--142. Google ScholarDigital Library 81. Kuper, G. M. and Vardi, M. Y. 1984. A new approach to database logic. In Proceedings of the 3th Symposium on Principles of Database Systems (PODS). ACM Press, 86--96. Google ScholarDigital Library 82. Kuper, G. M. and Vardi, M. Y. 1993. The Logical Data Model. ACM Trans. Database Syst. 18, 3, 379-- 413. Google ScholarDigital Library 83. Langou, B. and Mainguenaud, M. 1994. Graph data model operations for network facilities in a geographical information system. In Proceedings of the 6th International Symposium on Spatial Data Handling. Vol. 2. 1002--1019.Google Scholar 84. Lécluse, C., Richard, P., and Vélez, F. 1988. O2, an object-oriented data model. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM Press, 424--433. Google ScholarDigital Library 85. Levene, M. and Loizou, G. 1995. A graph-based data model and its ramifications. IEEE Trans. Knowl. Data Eng. 7, 5, 809--823. Google ScholarDigital Library 86. Levene, M. and Poulovassilis, A. 1990. The Hypernode model and its associated query language. In Proceedings of the 5th Jerusalem Conference on Information technology. IEEE Computer Society Press, 520--530. Google ScholarDigital Library 87. Levene, M. and Poulovassilis, A. 1991. An object-oriented data model formalised through hypergraphs. Data Knowl. Eng. 6, 3, 205--224. Google ScholarDigital Library 88. Mainguenaud, M. 1992. Simatic XT: A data model to deal with multi-scaled networks. Comput. Environ. Urban Syst. 16, 281--288.Google ScholarCross Ref 89. Mainguenaud, M. 1995. Modelling the network component of geographical information systems. Int. J. Geog. Inform. Syst. 9, 6, 575--593.Google ScholarCross Ref 90. Mannino, M. V. and Shapiro, L. D. 1990. Extensions to query languages for graph traversal problems. IEEE Trans. Knowl. Data Eng. 2, 3, 353--363. Google ScholarDigital Library 91. McGee, W. C. 1976. On user criteria for data model evaluation. ACM Trans. Database Syst. 1, 4, 370--387. Google ScholarDigital Library 92. McGuinness, D. L. and van Harmelen, F. 2004. OWL Web ontology language overview, W3C recommendation 10 (February). http://www.w3.org/TR/2004/REC-owl-features-20040210/.Google Scholar 93. Medeiros, C. B. and Pires, F. 1994. Databases for GIS. SIGMOD Record 23, 1 (March), 107--115. Google ScholarDigital Library 94. Mendelzon, A. O. and Wood, P. T. 1989. Finding regular simple paths in graph databases. In Proceedings of the 15th International Conference on Very Large Data Bases (VLDB). Morgan Kaufmann Publishers Inc., 185--193. Google ScholarDigital Library 95. Navathe, S. B. 1992. Evolution of data modeling for databases. Communications of the ACM 35, 9, 112--123. Google ScholarDigital Library 96. Nejdl, W., Siberski, W., and Sintek, M. 2003. Design issues and challenges for RDF- and schema-based peer-to-peer systems. SIGMOD Record 32, 3, 41--46. Google ScholarDigital Library 97. Newman, M. E. J. 2003. The structure and function of complex networks. SIAM Rev. 45, 2, 167--256.Google ScholarDigital Library 98. Olken, F. 2003. Tutorial on graph data management for biology. IEEE Computer Society Bioinformatics Conference (CSB).Google Scholar 99. Papakonstantinou, Y., Garcia-Molina, H., and Widom, J. 1995. Object exchange across heterogeneous information sources. In Proceedings of the 11th International Conference on Data Engineering (ICDE). IEEE Computer Society, 251--260. Google ScholarDigital Library 100. Paredaens, J. and Kuijpers, B. 1998. Data models and query languages for spatial databases. Data & Knowledge Engineering (DKE) 25, 1--2, 29--53. Google ScholarDigital Library 101. Paredaens, J., Peelman, P., and Tanca, L. 1995. G-Log: A graph-based query language. IEEE Trans. Knowl. Data Eng. 7, 3, 436--453. Google ScholarDigital Library 102. Peckham, J. and Maryanski, F. J. 1988. Semantic data models. ACM Comput. Surv. 20, 3, 153--189. Google ScholarDigital Library 103. Pepper, S. and Moore, G. 2001. XML topic maps (XTM) 1.0---TopicMaps.Org Specification. http://www.topicmaps.org/xtm/1.0/xtm1-20010806.html.Google Scholar 104. Poulovassilis, A. and Hild, S. G. 2001. Hyperlog: A graph-based system for database browsing, querying, and update. IEEE Trans. Knowl. Data Eng. 13, 2, 316--333. Google ScholarDigital Library 105. Poulovassilis, A. and Levene, M. 1994. A nested-graph model for the representation and manipulation of complex objects. ACM Trans. Inform. Syst. 12, 1, 35--68. Google ScholarDigital Library 106. Prud'hommeaux, E. and Seaborne, A. 2005. SPARQL Query Language for RDF, W3C Working Draft 21 July. http://www.w3.org/TR/2005/WD-rdf-sparql-query-20050721/.Google Scholar 107. Ramakrishnan, R. and Ullman, J. D. 1993. A survey of research on deductive database systems. J. Logic Prog. 23, 2, 125--149.Google ScholarCross Ref 108. Roussopoulos, N. and Mylopoulos, J. 1975. Using semantic networks for database management. In Proceedings of the International Conference on Very Large Data Bases (VLDB). ACM, 144--172. Google ScholarDigital Library 109. Samet, H. and Aref, W. G. 1995. Spatial data models and query processing. In Modern Database Systems. 338--360. Google ScholarDigital Library 110. Schewe, K.-D., Thalheim, B., Schmidt, J. W., and Wetzel, I. 1993. Integrity enforcement in object-oriented databases. In Proceedings of the 4th International Workshop on Foundations of Models and Languages for Data and Objects. Google ScholarDigital Library 111. Shasha, D., Wang, J. T. L., and Giugno, R. 2002. Algorithmics and applications of tree and graph searching. In Proceedings of the 21th Symposium on Principles of Database Systems (PODS). ACM Press, 39-- 52. Google ScholarDigital Library 112. Shekhar, S., Coyle, M., Goyal, B., Liu, D.-R., and Sarkar, S. 1997. Data models in geographic information systems. Commun. ACM 40, 4, 103--111. Google ScholarDigital Library 113. Sheng, L., Ozsoyoglu, Z. M., and Ozsoyoglu, G. 1999. A graph query language and its query processing. In Proceedings of the 15th International Conference on Data Engineering (ICDE). IEEE Computer Society, 572--581. Google ScholarDigital Library 114. Sheth, A., Aleman-Meza, B., Arpinar, I. B., Halaschek-Wiener, C., Ramakrishnan, C., Bertram, C., Warke, Y., Avant, D., Arpinar, F. S., Anyanwu, K., and Kochut, K. 2005. Semantic association identification and knowledge discovery for national security applications. J. Database Manag. 16, 1 (Jan-March), 33--53.Google ScholarCross Ref 115. Shipman, D. W. 1981. The functional data model and the data language DAPLEX. ACM Trans. Database Syst. 6, 1, 140--173. Google ScholarDigital Library 116. Silberschatz, A., Korth, H. F., and Sudarshan, S. 1996. Data models. ACM Comput. Surv. 28, 1, 105--108. Google ScholarDigital Library 117. Sowa, J. F. 1976. Conceptual graphs for a database interface. IBM J. Res. Devel. 20, 4, 336--357.Google ScholarDigital Library 118. Sowa, J. F. 1984. Conceptual Structures: Information Processing in Mind and Machine. Reading, MA, Addison-Wesley. Google ScholarDigital Library 119. Sowa, J. F. 1991. Principles of Semantic Networks: Explorations in the Representation of Knowledge. Morgan Kaufmann Publishers.Google Scholar 120. Stein, L. D. and Tierry-Mieg, J. 1999. AceDB: A genome database management system. Comput. Sci. Eng. 1, 3, 44--52.Google ScholarDigital Library 121. Tansel, A., Clifford, J., Gadia, S., Jajodia, S., Segev, A., and Snodgrass, R. T., Eds. 1993. Temporal Databases: Theory, Design, and Implementation. Benjamin-Cummings. Google ScholarDigital Library 122. Taylor, R. W. and Frank, R. L. 1976. CODASYL data-base management systems. ACM Comput. Surv. 8, 1, 67--103. Google ScholarDigital Library 123. Thalheim, B. 1991. Dependencies in Relational Databases. Leipzig, Teubner Verlag.Google Scholar 124. Thalheim, B. 1996. An overview on semantical constraints for database models. In Proceedings of the 6th International Conference Intellectual Systems and Computer Science.Google Scholar 125. Tompa, F. W. 1989. A data model for flexible hypertext database systems. ACM Trans. Inform. Syst. 7, 1, 85--100. Google ScholarDigital Library 126. Tsichritzis, D. C. and Lochovsky, F. H. 1976. Hierarchical data-base management: A survey. ACM Comput. Surv. 8, 1, 105--123. Google ScholarDigital Library 127. Tsvetovat, M., Diesner, J., and Carley, K. 2004. NetIntel: A database for manipulation of rich social network data. Tech. Rep. CMU-ISRI-04-135, Carnegie Mellon University, School of Computer Science, Institute for Software Research International.Google Scholar 128. Tuv, E., Poulovassilis, A., and Levene, M. 1992. A storage manager for the hypernode model. In Proceedings of the 10th British National Conference on Databases. Number 618 in LNCS. Springer-Verlag, 59--77. Google ScholarDigital Library 129. Vardi, M. Y. 1982. The complexity of relational query languages (extended abstract). In Proceedings of the 14th ACM Symposium on Theory of Computing (STOC). ACM Press, 137--146. Google ScholarDigital Library 130. Vassiliadis, P. and Sellis, T. 1999. A survey of logical models for OLAP Databases. SIGMOD Record 28, 4, 64--69. Google ScholarDigital Library 131. Vianu, V. 2003. A Web odyssey: From Codd to XML. SIGMOD Record 32, 2, 68--77. Google ScholarDigital Library 132. Watters, C. and Shepherd, M. A. 1990. A transient hypergraph-based model for data access. ACM Trans. Inform. Syst. 8, 2, 77--102. Google ScholarDigital Library 133. Weddell, G. E. 1992. Reasoning about functional dependencies generalized for semantic data models. ACM Trans. Database Syst. 17, 1, 32--64. Google ScholarDigital Library 134. Wellman, B., Salaff, J., Dimitrova, D., Garton, L., Gulia, M., and Haythornthwaite, C. 1996. Computer networks as social networks: collaborative work,telework, and virtual community. Ann. Rev. Sociol. 22, 213--238.Google ScholarCross Ref 135. Yannakakis, M. 1990. Graph-theoretic methods in database theory. In Proceedings of the 9th Symposium on Principles of Database Systems (PODS). ACM Press, 230--242. Google ScholarDigital Library 136. Zicari, R. 1991. A framework for schema updates in an object-oriented database system. In Proceedings of the 7th International Conference on Data Engineering (ICDE). IEEE Computer Society, 2--13. 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