Processing top-N relational queries by learning

  • Authors:

  • Liang Zhu;Weiyi Meng;Chunnian Liu;Wenzhu Yang;Dazhong Liu

  • Affiliations:

  • College of Computer Science and Technology, Beijing University of Technology, Beijing, China 100124 and Key Laboratory of Machine Learning and Computational Intelligence, School of Mathematics and ...;Department of Computer Science, State University of New York at Binghamton, Binghamton, USA 13902;College of Computer Science and Technology, Beijing University of Technology, Beijing, China 100124;Key Laboratory of Machine Learning and Computational Intelligence, School of Mathematics and Computer Science, Hebei University, Baoding, China 071002;Key Laboratory of Machine Learning and Computational Intelligence, School of Mathematics and Computer Science, Hebei University, Baoding, China 071002

  • Venue:
  • Journal of Intelligent Information Systems
  • Year:
  • 2010

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Abstract

A top-N selection query against a relation is to find the N tuples that satisfy the query condition the best but not necessarily completely. In this paper, we propose a new method for evaluating top-N queries against a relation. This method employs a learning-based strategy. Initially, this method finds and saves the optimal search spaces for a small number of random top-N queries. The learned knowledge is then used to evaluate new queries. Extensive experiments are carried out to measure the performance of this strategy and the results indicate that it is highly competitive with existing techniques for both low-dimensional and high-dimensional data. Furthermore, the knowledge base can be updated based on new user queries to reflect new query patterns so that frequently submitted queries can be processed most efficiently. The maintenance and stability of the knowledge base are also addressed in the paper.