Spark: top-k keyword query in relational databases

  • Authors:

  • Yi Luo;Xuemin Lin;Wei Wang;Xiaofang Zhou

  • Affiliations:

  • University of New South Wales, Sydney, Australia;University of New South Wales, Sydney, Australia;University of New South Wales, Sydney, Australia;University of Queensland, Brisbane, Australia

  • Venue:
  • Proceedings of the 2007 ACM SIGMOD international conference on Management of data
  • Year:
  • 2007

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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. Compared with previous approaches, our new ranking method is simple yet effective, and agrees with human perceptions. We also study efficient query processing methods for the new ranking method, and propose algorithms that have minimal accesses to the database. 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.