Download 02/25/05 Wavelet decomposition of data streams

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.