Social Interaction in Databases

  • Antonio Badia


The advent of the social web has made user-generated content a focus of research. Such content is already available in the form of tags, blogs, comments, etc., in many Web sites. Experience with social Web sites has shown that users tend to have a say about the data: they may interpret the same data in somewhat different ways, or may be able to add detail to it, enriching the existing contents. When users are able to enter information in a format-free manner, they tend to do so and may provide additional semantics to the data. The success of tagging clearly indicates that users are willing to provide content when this can be done in a manner that is natural to the users. Past research shows that the tags that users add to items in many Web sites are, as a whole, excellent descriptors of the contents of the items themselves and can be fruitfully used for several tasks, like improving search or clustering of the item set [1–3]. Overall, the amount of user-created content is growing at a fast rate.


Private Data Relational Algebra Database Schema Central Repository Relational Query 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was sponsored by NSF under grant CAREER IIS-0347555. The author is very grateful to his Program Manager, Maria Zemankova, for her support and patience.


  1. 1.
    Bao, S., Wu, X., Fei, B., Xue, G., Su, Z., Yu, Y.: Optimizing web search using social annotations. In: Proceedings of the WWW 2007 Conference, pp. 501–510. ACM, New York, NY (2007)Google Scholar
  2. 2.
    Escobar-Molano, M., Badia, A.: Exploiting Tags for Concept Extraction and Information Integration. In: Proceedings of the 5th International Conference on Collaborative Computing: Networking, Applications and Worksharing. IEEE Press (2009)Google Scholar
  3. 3.
    Golder, S., Huberman, B.A.: The structure of collaborative tagging systems. J. Inf. Sci. 32(2), 198–208 (2006)CrossRefGoogle Scholar
  4. 4.
    Melnik, S., Adya, A., Bernstein, P.A.: Compiling mappings to bridge applications and databases. J. ACM Trans. Database Syst. 33(4), 1–50 (2008)CrossRefGoogle Scholar
  5. 5.
    Wyss, C., Robertson, E.: Relational languages for metadata integration. ACM Trans. Database Syst. TODS 30(2), 624–660 (2005)CrossRefGoogle Scholar
  6. 6.
    Yu, B., Li, G., Ooi, B.C., Zhou, L.-Z.: One table stores all: enabling painless free-and-easy data publishing and sharing. In Proceedings of the 3rd Biennial Conference on Innovative Data Systems Research (CIDR), pp. 142–153, (2007)Google Scholar
  7. 7.
    Nørbåg, K.: Design issues in transaction-time temporal object database systems. In: Stuller, J., Pokorny, J., Thalheim, B., Masunaga, Y. (eds.) Current Issues in Databases and Information Systems. Lecture Notes in Computer Science, vol. 1884, pp. 371–378. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  8. 8.
    Jensen, C.S., Snodgrass, R.T.: Temporal data management. IEEE Trans. Knowl. Data Eng. TKDE 11, 36–44 (1999)CrossRefGoogle Scholar
  9. 9.
    Gupta, A., Mumick, I.S. (eds.): Materialized Views: Techniques, Implementations, and Applications. MIT, Cambridge, MA (1999)Google Scholar
  10. 10.
    Wilkinson, D.M., Huberman, B.A.: Cooperation and quality in Wikipedia. In: Proceedings of the 2007 International Symposium on Wikis, Montreal, Quebec, Canada, pp. 157–164 (2007).Google Scholar
  11. 11.
    Doan, A., Halevy, A.: Semantic integration research in the database community: A brief survey. AI Magazine, Special Issue on Semantic Integration, Spring (2005)Google Scholar
  12. 12.
    Kang, J., Naughton, J.: Schema matching using interattribute dependencies. IEEE Trans. Knowl. Data Eng. TKDE 20(10), 1393–1407 (2008)CrossRefGoogle Scholar
  13. 13.
    Sciore, E., Siegel, M., Rosenthal, A.: Using semantics values to facilitate interoperability among heterogeneous information systems. ACM Trans. Database Syst. 19, 254–290 (1994)CrossRefGoogle Scholar
  14. 14.
    Wasserman, S., Faust, K.: Social Network Analysis. Cambridge University Press, Cambridge (1994)Google Scholar
  15. 15.
    Vassiliadis, P., Papastefanatos, G., Vassiliou, Y., Sellis, T., Hellas, I.: Management of the evolution of database-centric information systems. Download from CiteSeerX at (2008)
  16. 16.
    Plangprasopchok, A., Lerman, K.: Exploiting social annotation for automatic resource discovery. In: Proceedings of AAAI workshop on Information Integration (2007)Google Scholar
  17. 17.
    Wu, X., Zhang, L.,Yu, Y.: Exploring social annotations for the semantic web. In: Proceedings of the WWW 2006 Conference (2006)Google Scholar
  18. 18.
    Li, R., Bao, S., Fei, B., Su, Z., Yu, Y.: Towards effective browsing of large scale social annotations. In Proceedings of the WWW 2007 Conference, pp. 943–952. ACM Press, New York, NY (2007)Google Scholar
  19. 19.
    Liu, H., Tang, T., Agarwal, N.: Tutorial on community detection and behavior. Study for social computing. Presented in the 1st IEEE International Conference on Social Computing (2009)Google Scholar
  20. 20.
    Agarwal, P.K., Xie, J., Yang, J., Yu, H.: Input-sensitive scalable continuous join query processing. ACM Trans. Database Syst. (TODS) 34(3) article 13 (2009)Google Scholar
  21. 21.
    Jayapandian, M., Jagadish, H.V.: Automating the design and construction of query forms. IEEE Trans. Knowl. Data Eng. TKDE 21(10), 1389–1402 (2009)CrossRefGoogle Scholar
  22. 22.
    Howe, B., Tanna, K., Turner, P., Maier, D.: Emergent semantics: towards self-organizing scientific metadata. In: Proceedings of the International Conference on Semantics of a Networked World (ICSNW), pp. 177–198 (2004)Google Scholar
  23. 23.
    Abiteboul, S., Polyzotis, N.: The data ring: community content sharing. In Proceedings of the 3rd Biennial Conference on Innovative Data Systems Research (CIDR), pp. 154–163 (2007)Google Scholar
  24. 24.
    Geerts, F., Kementsietsidis, A., Milano, D.: MONDRIAN: Annotating and querying databases through colors and blocks. In: Proceedings of the ICDE 2006 Conference (2006)Google Scholar
  25. 25.
    Bhagwat, D., Chiticariu, L., Tan, W., Vijayvargiya, G.: An annotation management system for relational database systems. In: Proceedings of the VLDB 2004 Conference, pp. 942–944 (2004)Google Scholar
  26. 26.
    Philippe, K.A. Ouksel, A. M., Catarci, T., Hacid, S., Illarramendi, A., Mecella, M., Mena, E., Neuhold, E.J., De, O., Risse, T., Scannapieco, M., Saltor, F., Santis, L.D., Spaccapietra, S., Staab, S., Studer, R.: Emergent semantics: principles and issues. In: Proceedings of the 9th International Conference on Database Systems for Advanced Applications (DASFAA 2004), pp. 25–38, Springer, HeidelbergGoogle Scholar
  27. 27.
    Santini, S., Gupta, A., Jain, R.: Emergent semantics through interaction in image databases. IEEE Trans. Knowl. Data Eng. 13(3), 337–351 (2001)CrossRefGoogle Scholar
  28. 28.
    Benjelloun, O., Sarma, A. D., Halevy, A., Widom, J.: Uldbs: Databases with uncertainty and lineage. In: Proceedings of the 32nd international conference on Very large data bases, VLDB Endowment, pp. 953–964 (2006)Google Scholar
  29. 29.
    Libkin, L.: Data exchange and incomplete information. In: Proceedings of the 25th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, Chicago, IL, USA, pp. 60–69 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Computer Engineering and Computer Science DepartmentUniversity of LouisvilleLouisvilleUSA

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