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02/25/05 Wavelet decomposition of data streams Speaker: Dragana Veljkovic Presentation slides Abstract: Streaming data arises in a variety of applications from mining of web logs and anomaly detection in network to satellite imagery. One of the problems that arise in streaming scenarios is the massive scale of updates of the underlying signal over time. Another problem is the need to correctly process the data in one pass. The goal of this research is to examine to what extent data streams can be summarized in a small amount of space so that accurate estimates can be provided for basic queries of the underlying signal. The techniques for computing small space representations are inspired by traditional wavelet based approximations that consist of specific linear projections of the underlying data. A general sketch-based method for computing various linear projections is presented. We also show it is possible use to provide pointwise and rangesum estimation of data streams. These methods use small amounts of space and per-item time while streaming through data and provide accurate representations as the experiments with real data show. References: A. C. Gilbert, Y. Kotidis, S. Muthukrishnan and M. J. Strauss, "One-pass wavelet decomposition of data streams," IEEE transactions on knowledge and data engineering, Vol. 15, No. 3, May/June 2003. A. C. Gilbert, Y. Kotidis, S. Muthukrishnan and M. J. Strauss, "Surfing wavelets on streams: one-pass summaries for approximate aggregate queries," Proceedings of the 27th VLDB Conference, Roma, Italy 2001. A. C. Gilbert, S. Guha, P. Indyk, Y. Kotidis, S. Muthukrishnan and M. J. Strauss, "Fast, small-space algorithms for approximate histogram maintenance," STOC ’02, May 19- 21, 2002, Montreal, Quebec, Canada.