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Energy-efficient skyline query processing and maintenance in sensor networks
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Conference on Information and Knowledge Management archive
Proceeding of the 17th ACM conference on Information and knowledge management table of contents
Napa Valley, California, USA
POSTER SESSION: Poster session 3: database table of contents
Pages 1471-1472  
Year of Publication: 2008
ISBN:978-1-59593-991-3
Authors
Weifa Liang  Australian National University, Canberra, ACT, Australia
Baichen Chen  Australian National University, Canberra, ACT, Australia
Jeffrey Xu Yu  Chinese Univ. of Hong Kong, Hong Kong, Hong Kong
Sponsors
ACM: Association for Computing Machinery
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
SIGIR: ACM Special Interest Group on Information Retrieval
Publisher
ACM  New York, NY, USA
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ABSTRACT

The skyline query, as an important operator in databases for multi-preference analysis and decision making, has received much attention recently due to its wide application backgrounds. In this paper, we consider the skyline query problem in Wireless Sensor Network with an objective to maximize the network lifetime by proposing filter-based distributed algorithms for skyline evaluation and maintenance. We also conduct preliminary experiments to evaluate the performance of the proposed algorithms. The experimental results demonstrate that the proposed algorithms significantly outperform existing algorithms on various datasets.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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H. Chen, S. Zhou and J. Guan. Towards energy-efficient skyline monitoring in wirelesssensor networks. Proc. of European Workshop on Wireless Sensor Networks, 2007.
 
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J. Xin, G. Wang, L. Chen, X. Zhang, and Z. Wang. Continuously maintaining sliding window skyline in a sensor network. Proc of DASFAA, 2007.

Collaborative Colleagues:
Weifa Liang: colleagues
Baichen Chen: colleagues
Jeffrey Xu Yu: colleagues