A practical approach for efficiently answering top-k relational queries

  • Authors:

  • Anteneh Ayanso;Paulo B. Goes;Kumar Mehta

  • Affiliations:

  • Department of Finance, Operations and Information Systems, Brock University, 500 Glenridge Avenue, St. Catharines, ON, Canada L2S 3A1;Department of Operations and Information Management, University of Connecticut, 2100 Hillside Road, U-1041IM, Storrs, CT 06269, USA;Decision Science and Management Information Systems, School of Management, George Mason University, 4400 University Drive MSN 5F4, Fairfax, VA 22030, USA

  • Venue:
  • Decision Support Systems
  • Year:
  • 2007

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Abstract

An increasing number of application areas now rely on obtaining the ''best matches'' to a given query as opposed to exact matches sought by traditional transactions. This type of exploratory querying (also called top-k querying) can significantly improve the performance of web-based applications such as consumer reviews, price comparisons and recommendations for products/services. Due to the lack of support for specialized indexes and/or data structures in relational database management systems (RDBMSs), recent research has focused on utilizing summary statistics (histograms) maintained by RDBMSs for translating the top-k request into a traditional range query. Because the RDBMS query engines are already optimized for execution of range queries, such approach has both practical as well as efficiency advantages. In this paper, we review the strengths and weaknesses of common histogram construction techniques with regard to their structural characteristics, accuracy in approximating the true distribution of the underlying data, and implications for top-k retrieval. We also present our top-k retrieval strategy (Query-Level Optimal Cost Strategy - QLOCS) and demonstrate its ''histogram-independent'' performance. Based on comparative experimental and statistical analyses with the best-known histogram-based strategy in the literature, we show that QLOCS is not only more efficient but also provides more consistent performance across commonly used histogram types in RDBMSs.