Advertisement

Retune: Retrieving and Materializing Tuple Units for Effective Keyword Search over Relational Databases

  • Guoliang Li
  • Jianhua Feng
  • Lizhu Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5231)

Abstract

The existing approaches of keyword search over relational databases always identify the relationships between tuples on the fly, which are rather inefficient as such relational relationships are very rich in the underlying databases. Alternatively, this paper proposes an alternative way by retrieving and materializing tuple units for facilitating the online processing of keyword search. We first propose a novel concept of tuple   units, which are composed of the relevant tuples connected by the primary-foreign-key relationships. We then demonstrate how to generate and materialize the tuple units, and the technique for generating the tuple units can be done by issuing SQL statements and thus can be performed directly on the underlying RDBMS without modification to the database engine. Finally, we examine the techniques of indexing and ranking to improve the search efficiency and search quality. We have implemented our method and the experimental results show that our approach achieves much better search performance, and outperforms the alternative literatures significantly.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
  3. 3.
  4. 4.
    Agrawal, S., Chaudhuri, S., Das, G.: Dbxplorer: A system for keyword-based search over relational databases. In: ICDE, pp. 5–16 (2002)Google Scholar
  5. 5.
    Arai, B., Das, G., Gunopulos, D., Koudas, N.: Anytime measures for topk algorithms. In: VLDB (2007)Google Scholar
  6. 6.
    Bhalotia, G., Hulgeri, A., Nakhe, C., Chakrabarti, S., Sudarshan, S.: Keyword searching and browsing in databases using banks. In: ICDE, pp. 431–440 (2002)Google Scholar
  7. 7.
    Cohen, S., Mamou, J., Kanza, Y., Sagiv, Y.: Xsearch: A semantic search engine for xml. In: VLDB, pp. 45–56 (2003)Google Scholar
  8. 8.
    Ding, B., Yu, J.X., Wang, S., et al.: Finding top-k min-cost connected trees in databases. In: ICDE (2007)Google Scholar
  9. 9.
    Guo, L., Shanmugasundaram, J., Yona, G.: Topology search over biological databases. In: ICDE (2007)Google Scholar
  10. 10.
    Guo, L., Shao, F., Botev, C., Shanmugasundaram, J.: Xrank: Ranked keyword search over XML documents. In: SIGMOD, pp. 16–27 (2003)Google Scholar
  11. 11.
    He, H., Wang, H., Yang, J., Yu, P.: Blinks: Ranked keyword searches on graphs. In: SIGMOD (2007)Google Scholar
  12. 12.
    Hristidis, V., Gravano, L., Papakonstantinou, Y.: Efficient ir-style keyword search over relational databases. In: VLDB, pp. 850–861 (2003)Google Scholar
  13. 13.
    Hristidis, V., Papakonstantinou, Y.: Discover: Keyword search in relational databases. In: VLDB, pp. 670–681 (2002)Google Scholar
  14. 14.
    Hua, M., Pei, J., Fu, A.W.C., Lin, X., Leung, H.-F.: Efficiently answering top-k typicality queries on large databases. In: VLDB (2007)Google Scholar
  15. 15.
    Kacholia, V., Pandit, S., et al.: Bidirectional expansion for keyword search on graph databases. In: VLDB, pp. 505–516 (2005)Google Scholar
  16. 16.
    Kimelfeld, B., Sagiv, Y.: Finding and approximating top-k answers in keyword proximity search. In: PODS (2006)Google Scholar
  17. 17.
    Li, G., Feng, J., Wang, J., Zhou, L.: Efficient keyword search for valuable lcas over XML documents. In: CIKM (2007)Google Scholar
  18. 18.
    Li, G., Feng, J., Wang, J., Zhou, L.: Race: Finding and ranking compact connected trees for keyword proximity search over xml documents. In: WWW (2008)Google Scholar
  19. 19.
    Li, G., Feng, J., Wang, J., Zhou, L.: Sailer: An effective search engine for unified retrieval of heterogeneous XML and web documents. In: WWW (2008)Google Scholar
  20. 20.
    Li, G., Feng, J., Zhou, L.: Progressive ranking for efficient keyword search over relational databases. In: BNCOD (2008)Google Scholar
  21. 21.
    Li, G., Ooi, B.C., Feng, J., Wang, J., Zhou, L.: Ease: An effective 3-in-1 keyword search methord for unstructured, semi-structured and structured data. In: SIGMOD (2008)Google Scholar
  22. 22.
    Liu, F., Yu, C., Meng, W., Chowdhury, A.: Effective keyword search in relational databases. In: SIGMOD, pp. 563–574 (2006)Google Scholar
  23. 23.
    Luo, Y., Lin, X., Wang, W., Zhou, X.: Spark: Top-k keyword query in relational databases. In: SIGMOD (2007)Google Scholar
  24. 24.
    Markowetz, A., Yang, Y., Papadias, D.: Keyword search on relational data streams. In: SIGMOD (2007)Google Scholar
  25. 25.
    Sayyadian, M., LeKhac, H., Doan, A., Gravano, L.: Efficient keyword search across heterogeneous relational databases. In: ICDE (2007)Google Scholar
  26. 26.
    Schnaitter, K., Spiegel, J., Polyzotis, N.: Depth estimation for ranking query optimization. In: VLDB (2007)Google Scholar
  27. 27.
    Shao, F., Guo, L., Botev, C., Bhaskar, A., Chettiar, M., Yang, F., Shanmugasundaram, J.: Efficient keyword search over virtual xml views. In: VLDB (2007)Google Scholar
  28. 28.
    Su, Q., Widom, J.: Indexing relational database content offline for efficient keyword-based search. In: IDEAS (2005)Google Scholar
  29. 29.
    Xu, Y., Papakonstantinou, Y.: Efficient keyword search for smallest lcas in XML databases. In: SIGMOD, pp. 527–538 (2005)Google Scholar
  30. 30.
    Yu, B., Li, G., Sollins, K., Tung, A.K.H.: Effective keyword-based selection of relational databases. In: SIGMOD, pp. 139–150 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Guoliang Li
    • 1
  • Jianhua Feng
    • 1
  • Lizhu Zhou
    • 1
  1. 1.Department of Computer Science and TechnologyTsinghua UniversityBeijingP.R. China

Personalised recommendations