Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Dynamic Maintenance of Wavelet-Based Histograms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
One-pass wavelet synopses for maximum-error metrics
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
IEEE Transactions on Information Technology in Biomedicine
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In this article, we introduce a continuous data stream reduction method using wavelets summarization. Especially we consider storing a plenty of past data stream into stable storage (flash memory or micro HDD) rather than keeping only recent streaming data allowable in memory, because data stream mining and tracking of past data stream are often required. In the general method using wavelets, a specific amount of streaming data from a sensor is periodically compressed into fixed size and the fixed amount of compressed data (selected wavelet coefficients) is stored into stable storage. However, our method flexibly adjusts the number of selected wavelet coefficients for each local time section. Experimental results with some real world data show that our flexible approach has lower estimation error than the general fixed approach.