PreCN: preprocessing candidate networks for efficient keyword search over databases

  • Authors:

  • Jun Zhang;Zhaohui Peng;Shan Wang;Huijing Nie

  • Affiliations:

  • School of Information, Renmin University of China, Beijing, P. R. China;School of Information, Renmin University of China, Beijing, P. R. China;School of Information, Renmin University of China, Beijing, P. R. China;School of Information, Renmin University of China, Beijing, P. R. China

  • Venue:
  • WISE'06 Proceedings of the 7th international conference on Web Information Systems
  • Year:
  • 2006

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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(Gts) 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 Gts. Extensive experiments show that the approach PreCN is efficient and effective.