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Ranked Document Selection

  • J. Ian Munro
  • Gonzalo Navarro
  • Rahul Shah
  • Sharma V. Thankachan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8503)

Abstract

Let \({\cal D}\) be a collection of string documents of n characters in total. The top-k document retrieval problem is to preprocess \({\cal D}\) into a data structure that, given a query (P,k), can return the k documents of \({\cal D}\) most relevant to pattern P. The relevance of a document d for a pattern P is given by a predefined ranking function w(P,d). Linear space and optimal query time solutions already exist for this problem.

In this paper we consider a novel problem, document selection queries, which aim to report the kth document most relevant to P (instead of reporting all top-k documents). We present a data structure using O(n log ε n) space, for any constant ε > 0, answering selection queries in time O(logk / loglogn), and a linear-space data structure answering queries in time O(logk), given the locus node of P in a (generalized) suffix tree of \({\cal D}\). We also prove that it is unlikely that a succinct-space solution for this problem exists with poly-logarithmic query time.

Keywords

Query Time Document Retrieval Prime Node Query Algorithm Lower Common Ancestor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • J. Ian Munro
    • 1
  • Gonzalo Navarro
    • 2
  • Rahul Shah
    • 3
  • Sharma V. Thankachan
    • 1
  1. 1.Cheriton School of CSUniv. WaterlooCanada
  2. 2.Dept. of CSUniv. ChileChile
  3. 3.School of EECSLouisiana State Univ.USA

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