Bs-tree: a self-tuning index of moving objects

Authors:
Nan Chen;Lidan Shou;Gang Chen;Ke Chen;Yunjun Gao
Affiliations:
College of Computer Science, Zhejiang University, China;College of Computer Science, Zhejiang University, China;College of Computer Science, Zhejiang University, China;College of Computer Science, Zhejiang University, China;College of Computer Science, Zhejiang University, China
Venue:
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
Year:
2010

Citing 13
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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.