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Selective-NRA Algorithms for Top-k Queries

  • Jing Yuan
  • Guang-Zhong Sun
  • Ye Tian
  • Guoliang Chen
  • Zhi Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5446)

Abstract

Efficient processing of top-k queries has become a classical research area recently since it has lots of application fields. Fagin et al. proposed the “middleware cost” for a top-k query algorithm. In some databases there is no way to perform a random access, Fagin et al. proposed NRA (No Random Access) algorithm for this case. In this paper, we provided some key observations of NRA. Based on them, we proposed a new algorithm called Selective-NRA (SNRA) which is designed to minimize the useless access of a top-k query. However, we proved the SNRA is not instance optimal in Fagin’s notion and we also proposed an instance optimal algorithm Hybrid-SNRA based on algorithm SNRA. We conducted extensive experiments on both synthetic and real-world data. The experiments showed SNRA (Hybrid-SNRA) has less access cost than NRA. For some instances, SNRA performed 50% fewer accesses than NRA .

Keywords

Random Access Aggregation Function Access Cost Good Competitor Cup98 Data 
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 2009

Authors and Affiliations

  • Jing Yuan
    • 1
  • Guang-Zhong Sun
    • 1
  • Ye Tian
    • 1
  • Guoliang Chen
    • 1
  • Zhi Liu
    • 1
  1. 1.MOE-MS Key Laboratory of Multimedia Computing and Communication, Department of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefeiP.R. China

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