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Getting Critical Categories of a Data Set

  • Cheqing Jin
  • Yizhen Zhang
  • Aoying Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6897)

Abstract

Ranking query that is widely used in various applications is a fundamental kind of queries in the database management field. Although most of the existing work on ranking query focuses on getting top-k high-score tuples from a data set, this paper focuses on getting top-k critical categories from a data set, where each category is a data item in the nominal attribute or a combination of data items from more than one nominal attribute. To describe each category precisely, we use a data distribution that comes from the score attribute to represent each category, so that the set consisting of all categories can be treated as a probabilistic data set. In this paper, we devise a novel method to handle this issue. Analysis in theorem and experimental results show the effectiveness and efficiency of the proposed method.

Keywords

critical category ranking query possible world 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Cheqing Jin
    • 1
  • Yizhen Zhang
    • 1
  • Aoying Zhou
    • 1
  1. 1.Shanghai Key Laboratory of Trustworthy Computing, Software Engineering InstituteEast China Normal UniversityChina

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