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Exact and Approximate Generic Multi-criteria Top-k Query Processing

  • Mehdi Badr
  • Dan VodislavEmail author
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8980)

Abstract

Many algorithms for multi-criteria top-\(k\) query processing with ranking predicates have been proposed, but little effort has been directed toward genericity, i.e. supporting any type of access to the lists of predicate scores (sorted and/or random), or any access cost settings. In this paper we propose a general approach to exact and approximate generic top-\(k\) processing. To this end, we propose a general framework (GF) for generic top-\(k\) processing, able to express any top-\(k\) algorithm and present within this framework a first comparison between generic algorithms. In previous work, we proposed BreadthRefine (BR), a generic algorithm that considers the current top-\(k\) candidates as a whole instead of focusing on the best candidate for score refinement, then we compared it with specific top-\(k\) algorithms. In this paper, we propose two variants of existing generic strategies and experimentally compare them with the BR breadth-first strategy, showing that BR leads to better execution costs. We also extend the notion of \(\theta \)-approximation to the GF framework and present a first experimental study of the approximation potential of top-\(k\) algorithms on early stopping.

Keywords

Top-\(k\) query processing Ranking Multi-criteria information retrieval 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.ETISENSEA - University of Cergy-Pontoise - CNRSCergyFrance
  2. 2.TRIMANESaint-Germain-en-LayeFrance

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