Boolean + ranking: querying a database by k-constrained optimization

  • Authors:

  • Zhen Zhang;Seung-won Hwang;Kevin Chen-Chuan Chang;Min Wang;Christian A. Lang;Yuan-chi Chang

  • Affiliations:

  • University of Illinois at Urbana-Champaign;Pohang University of Science and Technology;University of Illinois at Urbana-Champaign;IBM T.J. Watson Research Center;IBM T.J. Watson Research Center;IBM T.J. Watson Research Center

  • Venue:
  • Proceedings of the 2006 ACM SIGMOD international conference on Management of data
  • Year:
  • 2006

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Abstract

The wide spread of databases for managing structured data, compounded with the expanded reach of the Internet, has brought forward interesting data retrieval and analysis scenarios to RDBMS. In such settings, queries often take the form of k-constrained optimization, with a Boolean constraint and a numeric optimization expression as the goal function, retrieving only the top-k tuples. This paper proposes the concept of supporting such queries, as their nature implies, by a functional optimization machinery over the search space of multiple indices. To realize this concept, we combine the dual perspectives of discrete state search (from the view of indices) and continuous function optimization (from the view of goal functions). We present, as the marriage of the two perspectives, the OPT* framework, which encodes k-constrained optimization as an A* search over the composite space of multiple indices, driven by functional optimization for providing tight heuristics. By processing queries as optimization, OPT* significantly outperforms baseline approaches, with up to 3 orders of magnitude margins.