Being picky: processing top-k queries with set-defined selections

  • Authors:

  • Aleksandar Stupar;Sebastian Michel

  • Affiliations:

  • Saarland University, Saarbruecken, Germany;Saarland University, Saarbruecken, Germany

  • Venue:
  • Proceedings of the 21st ACM international conference on Information and knowledge management
  • Year:
  • 2012

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Abstract

Focusing on the top-K items according to a ranking criterion constitutes an important functionality in many different query answering scenarios. The idea is to read only the necessary information---mostly from secondary storage---with the ultimate goal to achieve low latency. In this work, we consider processing such top-K queries under the constraint that the result items are members of a specific set, which is provided at query time. We call this restriction a set-defined selection criterion. Set-defined selections drastically influence the pros and cons of an id-ordered index vs. a score-ordered index. We present a mathematical model that allows to decide at runtime which index to choose, leading to a combined index. To improve the latency around the break even point of the two indices, we show how to benefit from a partitioned score-ordered index and present an algorithm to create such partitions based on analyzing query logs. Further performance gains can be enjoyed using approximate top-K results, with tunable result quality. The presented approaches are evaluated using both real-world and synthetic data.