PreCN: Preprocessing Candidate Networks for Efficient Keyword Search over Databases

  • Jun Zhang
  • Zhaohui Peng
  • Shan Wang
  • Huijing Nie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4255)

Abstract

Keyword Search Over Relational Databases(KSORD) has attracted much research interest since casual users or Web users can use the techniques to easily access databases through free-form keyword queries, just like searching the Web. However, it is a critical issue that how to improve the performance of KSORD systems. In this paper, we focus on the performance improvement of schema-graph-based online KSORD systems and propose a novel Preprocessing Candidate Network(PreCN) approach to support efficient keyword search over relational databases. Based on a given database schema, PreCN reduces CN generation time by preprocessing the maximum Tuple Sets Graph(G ts ) to generate CNs in advance and to store them in the database. When a user query comes, its CNs will be quickly retrieved from the database instead of being temporarily generated through a breadth-first traversal of its G ts . Extensive experiments show that the approach PreCN is efficient and effective.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Wang, S., Zhang, K.: Searching Databases with Keywords. Journal of Computer Science and Technology 20(1), 55–62 (2005)CrossRefGoogle Scholar
  2. 2.
    Bergman, M.K.: The deep web: Surfacing hidden value. White Paper, Bright Plannet (2000)Google Scholar
  3. 3.
    Qi, S., Jennifer, W.: Indexing Relational Database Content Offline for Efficient Keyword-Based Search. In: Proceeding of IDEAS, pp. 297–306 (2005)Google Scholar
  4. 4.
    Wen, J., Wang, S.: SEEKER: Keyword-based Information Retrieval Over Relational Data-bases. Journal of Software (2005)Google Scholar
  5. 5.
    Zhang, K.: Research on New Preprocess-ing Technology for Keyword Search in Databases. PhD thesis of Renmin University of China (2005)Google Scholar
  6. 6.
    Hristidis, V., Papakonstantinou, Y.: DISCOVER: Keyword Search in Relational Databases. In: VLDB, pp. 670–681 (2002)Google Scholar
  7. 7.
    Hristidis, V., Gravano, L., Papakonstantinou, Y.: Efficient IR-Style Keyword Search over Relational Databases. In: VLDB, pp. 850–861 (2003)Google Scholar
  8. 8.
    Agrawal, S., Chaudhuri, S., Das, G.: DBXplorer:A System for keyword Search over Relational Databases. In: ICDE, pp. 5–16 (2002)Google Scholar
  9. 9.
    Bhalotia, G., Hulgeri, A., Nakhe, C., et al.: Keyword Searching and Browsing in Databases using BANKS. In: ICDE, pp. 431–440 (2002)Google Scholar
  10. 10.
    Kacholia, V., Pandit, S., Chakrabarti, S., et al.: Bidirectional Expansion For Keyword Search on Graph Databases. In: VLDB 2005, pp. 505–516 (2005)Google Scholar
  11. 11.
    Jansen, B., Spink, A., Saracevic, T.: Real life, real users, and real needs: A study and analysis of user queries on the web. Information Processing and Management 36(2), 207–227 (2000)CrossRefGoogle Scholar
  12. 12.
    Zhang, J., Peng, Z., Wang, S., Nie, H.: CLASCN: Candidate Network Selection Supporting Efficient Top-k Keyword Queries over Databases. Technical Report, School of Information, Renmin University of China (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jun Zhang
    • 1
    • 2
    • 3
  • Zhaohui Peng
    • 1
    • 2
  • Shan Wang
    • 1
    • 2
  • Huijing Nie
    • 1
    • 2
  1. 1.School of InformationRenmin University of ChinaBeijingP. R. China
  2. 2.Key Laboratory of Data Engineering and Knowledge Engineering(Renmin University of China)MOEBeijingP.R. China
  3. 3.Computer Science and Technology CollegeDalian Maritime UniversityDalianP.R. China

Personalised recommendations