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Finding the Most Desirable Skyline Objects

  • Yunjun Gao
  • Junfeng Hu
  • Gencai Chen
  • Chun Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5982)

Abstract

This paper introduces a new operator, namely the most desirable skyline object (MDSO) query, to identify manageable size of truly interesting skyline objects. Given a set of multi-dimensional objects and an integer k, a MDSO query retrieves the most preferable k skyline objects, based on the newly defined ranking criterion that considers, for each skyline object s, the number of objects dominated by s and their accumulated (potential) weight. We present the ranking criterion, formalize the MDSO query, and develop two algorithms for processing MDSO queries assuming that the dataset is indexed by a traditional data-partitioning index. Extensive experiments demonstrate the performance of the proposed algorithms.

Keywords

Preference Score Skyline Query Ranking Criterion Skyline Point Line Object 
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-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yunjun Gao
    • 1
  • Junfeng Hu
    • 2
  • Gencai Chen
    • 1
  • Chun Chen
    • 1
  1. 1.College of Computer Science and TechnologyZhejiang University 
  2. 2.School of ComputingNational University of Singapore 

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