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\(\textrm{B}^s\)-tree: A Self-tuning Index of Moving Objects

  • Nan Chen
  • Lidan Shou
  • Gang Chen
  • Ke Chen
  • Yunjun Gao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5982)

Abstract

Self-tuning database is a general paradigm for the future development of database systems. However, in moving object database, a vibrant and dynamic research area of the database community, the need for self-tuning has so far been overlooked. None of the existing spatio-temporal indexes can maintain high performance if the proportion of query and update operations varies significantly in the applications. We study the self-tuning indexing techniques which balance the query and update performances for optimal overall performance in moving object databases. In this paper, we propose a self-tuning framework which relies on a novel moving object index named \(\textrm{B}^s\)-tree. This framework is able to optimize its own overall performance by adapting to the workload online without interrupting the indexing service. We present various algorithms for the \(\textrm{B}^s\)-tree and the tuning techniques. Our extensive experiments show that the framework is effective, and the \(\textrm{B}^s\)-tree outperforms the existing indexes under different circumstances.

Keywords

spatio-temporal database moving object index self-tuning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Nan Chen
    • 1
  • Lidan Shou
    • 1
  • Gang Chen
    • 1
  • Ke Chen
    • 1
  • Yunjun Gao
    • 1
  1. 1.College of Computer ScienceZhejiang UniversityChina

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