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AbIx: An Approach to Content-Based Approximate Query Processing in Peer-to-Peer Data Systems

Chao-Kun Wang1, Jian-Min Wang1, Jia-Guang Sun1, Sheng-Fei Shi2, and Hong Gao2   

  1. 1School of Software, Tsinghua University, Beijing 100084, China 2School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
  • Received:2006-05-01 Revised:2007-01-21 Online:2007-03-10 Published:2007-03-10

In recent years there has been a significant interest in peer-to-peer (P2P) environments in the community of data management. However, almost all work, so far, is focused on exact query processing in current P2P data systems. The autonomy of peers also is not considered enough. In addition, the system cost is very high because the information publishing method of shared data is based on each document instead of document set. In this paper, abstract indices (AbIx) are presented to implement content-based approximate queries in centralized, distributed and structured P2P data systems. It can be used to search as few peers as possible but get as many returns satisfying users' queries as possible on the guarantee of high autonomy of peers. Also, abstract indices have low system cost, can improve the query processing speed, and support very frequent updates and the set information publishing method. In order to verify the effectiveness of abstract indices, a simulator of 10,000 peers, over 3 million documents is made, and several metrics are proposed. The experimental results show that abstract indices work well in various P2P data systems.

Key words: keyword spotting; keyword spotter; vocabulary independent; acoustic modeling; continuous speech recognition;



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