SPARK2: Top-k Keyword Query in Relational Databases

  • Authors:

  • Yi Luo;Wei Wang;Xuemin Lin;Xiaofang Zhou;Jianmin Wang;Kequi Li

  • Affiliations:

  • Laboratory Le2i of CNRS, Dijon;University of New South Wales, Sydney;University of New South Wales, Sydney;University of Queensland, Brisbane;Tsinghua University, Beijing;Dalian University of Technology, Beijing

  • Venue:
  • IEEE Transactions on Knowledge and Data Engineering
  • Year:
  • 2011

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Abstract

With the increasing amount of text data stored in relational databases, there is a demand for RDBMS to support keyword queries over text data. As a search result is often assembled from multiple relational tables, traditional IR-style ranking and query evaluation methods cannot be applied directly. In this paper, we study the effectiveness and the efficiency issues of answering top-k keyword query in relational database systems. We propose a new ranking formula by adapting existing IR techniques based on a natural notion of virtual document. We also propose several efficient query processing methods for the new ranking method. We have conducted extensive experiments on large-scale real databases using two popular RDBMSs. The experimental results demonstrate significant improvement to the alternative approaches in terms of retrieval effectiveness and efficiency.