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Data mining: concepts and techniques
Data mining: concepts and techniques
Wireless sensor networks for habitat monitoring
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Approximate Query Processing: Taming the TeraBytes
Proceedings of the 27th International Conference on Very Large Data Bases
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Time-series prediction with applications to traffic and moving objects databases
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Compressing historical information in sensor networks
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
XWAVE: optimal and approximate extended wavelets
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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We evaluated several multivariate stream data reduction techniques that can be used in sensor network applications. The evaluated techniques include Wavelet-based methods, sampling, hierarchical clustering, and singular value decomposition (SVD). We tested the reduction methods over the range of different parameters including data reduction rate, data types, number of dimensions and data window size of the input stream. Both real and synthetic time series data were used for the evaluation. The results of experiments suggested that the reduction techniques should be evaluated in the context of applications, as different applications generate different types of data and that has a substantial impact on the performance of different reduction methods. The findings reported in this paper can serve as a useful guideline for sensor network design and construction.