Generating Social Relationships from Relational Databases for Graph Database Creation and Social Business Intelligence Management

  • Frank S. C. TsengEmail author
  • Annie Y. H. Chou
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 858)


Social media services create an extremely exciting global network, together with a burst of knowledge regarding product information, marketing solicitation or public issues discussion. The community is becoming an influential repository for the analysis of counter-terrorism, human resource management, marketing, service design and any other kinds of business activities. However, the collection of social relationships between individuals, among places or even objects is a daunting task. Therefore, we need an effective way to generate pre-existed relationships as a basis for the analysis of social networks. In this paper, we propose a framework for generating relationships from traditional relational databases, which have been tremendously employed to store commercial data, including customers, staffs, and other related stuffs. We explore the processes of generating explicit and implicit relationships from the viewpoint of Entity-Relationship Model, where the former can be derived directly from the E-R schema, and the latter can be generated from the viewpoint of data semantics. The derivation can be conducted by defining rules, expressing pattern matching in query statements, or even applying data mining algorithms. The obtained result can be used as a stem to evolve to connect the social networks of customers, product manufacturers and related staffs. Based on our framework, the constructed graph database can be easily linked to traditional relational technologies, including data warehouses, knowledge management or business intelligence. Our framework provides a progressive evolution from relational database to graph database, with the pre-existed applications to link with social intelligence seamlessly. We believe this framework helps us pave a way for developing people-centric technologies for the needs of social resource integration and social business intelligence in every domain.


Entity-relationship model Graph database Social business intelligence Social networking 



This research is supported by the Ministry of Science and Technology, Taiwan, ROC, under Contract No. MOST 107-2410-H-992-016-MY2.


  1. 1.
    Amer-Yahia, S., Lakshmanan, L., Yu, C.: Socialscope: enabling information discovery on social content sites. In: Proceedings of the 4th Biennial Conference on Innovative Data Systems Research (CIDR), pp. 1–11, January 2009Google Scholar
  2. 2.
    Barrat, M.B.A., Vespignani, A.: Dynamical Processes on Complex Networks. Cambridge University Press, New York (2008)CrossRefGoogle Scholar
  3. 3.
    Becker, H., Iter, D., Naaman, M., Gravano, L.: Identifying content for planned events across social media sites. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 533–542, February 2012Google Scholar
  4. 4.
    Bondy, J.A., Murty, U.S.R.: Graph Theory with Applications, The Macmillan Press Ltd., New York (1976)CrossRefGoogle Scholar
  5. 5.
    Christen, P.: Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplication Detection. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    De Choudhury, M., Lin, Y.R., Sundaram, H., Candan, K.S., Xie, L., Kelliher, A.: How does the data sampling strategy impact the discovery of information diffusion in social media? In: Proceedings of the 4th International AAAI Conference on Weblogs and Social Media, pp. 34–41, March 2010Google Scholar
  7. 7.
    Dewan, P., Gupta, M., Goyal, K., Kumaraguru, P.: Multiosn: realtime monitoring of real world events on multiple online social media. In: Proceedings of the 2013 ACM 5th IBM Collaborative Academia Research Exchange Workshop, p. 6, October 2013Google Scholar
  8. 8.
    Dou, W., Wang, K., Ribarsky, W., Zhou, M.: Event detection in social media data. In: Proceedings of the IEEE VisWeek Workshop on Interactive Visual Text Analytics-Task Driven Analytics of Social Media Content, pp. 971–980, October 2012Google Scholar
  9. 9.
    Draxler, C.: A Powerful Prolog to SQL Compiler, Technical report, CIS Centre for Information and Language Processing, Ludwig-Maximilians-Universitat, Munchen (1993). Also implemented as packs for SWI-Prolog.
  10. 10.
    Easley, D., Kleinberg, J.: Networks, Crowds, and Markets: Reasoning about a Highly Connected World, chap. 5, Cambridge University Press, New York (2010)Google Scholar
  11. 11.
    Floyd, R.W.: Algorithm 97: shortest path. Commun. ACM 5(6), 345 (1962)CrossRefGoogle Scholar
  12. 12.
    Getoor, L., Machanavajjhala, A.: Entity resolution: theory, practice & open challenges. Proc. VLDB Endow. 5(12), 2018–2019 (2012)CrossRefGoogle Scholar
  13. 13.
    Ho, L.Y., Wu, J.J., Liu, P.: Distributed graph database for large-scale social computing. In: Proceedings of the 5th International Conference on Cloud Computing (2012)Google Scholar
  14. 14.
    Hsiao, D.K., Kamel, M.N.: Heterogeneous databases: proliferations, issues, and solutions. IEEE Trans. Knowl. Data Eng. 1(1), 45–62 (1989)CrossRefGoogle Scholar
  15. 15.
    Hsiao, D.K.: Federated databases and systems: part I—a tutorial on their data sharing. VLDB J. 1(1), 127–179 (1992)CrossRefGoogle Scholar
  16. 16.
    Hsiao, D.K.: Federated databases and systems: part i—a tutorial on their resource consolidation. VLDB J. 1(2), 285–310 (1992)CrossRefGoogle Scholar
  17. 17.
    Nuutila, E.: Efficient Transitive Closure Computation in Large Digraphs, Acta Polytechnica Scandinavica, Mathematics and Computing in Engineering Series No. 74, Helsinki 1995, 124 pages. Published by the Finnish Academy of Technology (1995)Google Scholar
  18. 18.
    Passant, A., Breslin, J.: The semantic social web: ICWSM tutorial. A Tutorial Presented on the 4th International Conference on Web and Social Media (ICWSM) (2010)Google Scholar
  19. 19.
    Rehman, N. U., Mansmann, S., Weiler, A., Scholl, M.H.: Building a data warehouse for Twitter stream exploration. In: Proceedings of the 2012 IEEE International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012), pp. 1341–1348, August 2012Google Scholar
  20. 20.
    Tseng, F.S.C., Chou, A.Y.H.: The concept of document warehousing for multi-dimensional modeling of textual-based business intelligence. Decis. Support Syst. 42, 727–744 (2006)CrossRefGoogle Scholar
  21. 21.
    Tseng, F.S.C., Lin, W.-P.: D-Tree: a multi-dimensional indexing structure for constructing document warehouses. J. Inf. Sci. Eng. 22(4), 819–841 (2006)MathSciNetGoogle Scholar
  22. 22.
    Tseng, F.S.C.: Design of a multi-dimensional query expression for document warehouses. Inf. Sci. 174(1–2), 55–79 (2005)CrossRefGoogle Scholar
  23. 23.
    Tseng, F.S.C., Chou, A.Y.H.: The concept of social warehousing for on-line social networking management of synecological business intelligence. In: International Conference on Business and Information, Osaka, Japan, 4–6 July 2014Google Scholar
  24. 24.
    Vicknair, C., Macias, M., Zhao, Z., Nan, X., Chen, Y., Wilkins, D.: A comparison of a graph database and a relational database: a data provenance perspective. In: Proceedings of the 48th ACM SE Annual Southeast Regional Conference, pp. 42–48 (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Information ManagementNational Kaohsiung University of Science and Technology (First Campus)KaohsiungTaiwan, ROC
  2. 2.Department of Computer and Information ScienceROC Military AcademyKaohsiungTaiwan, ROC

Personalised recommendations