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<p><b>Abstract</b>—We present techniques for computing small space representations of massive data streams. These are inspired by traditional wavelet-based approximations that consist of specific linear projections of the underlying data. We present general “sketch”-based methods for capturing various linear projections and use them to provide pointwise and rangesum estimation of data streams. These methods use small amounts of space and per-item time while streaming through the data and provide accurate representation as our experiments with real data streams show.</p>
Data streams, wavelets, randomized algorithms, approximate queries.
Anna C. Gilbert, Yannis Kotidis, S. Muthukrishnan, Martin J. Strauss, "One-Pass Wavelet Decompositions of Data Streams", IEEE Transactions on Knowledge & Data Engineering, vol. 15, no. , pp. 541-554, May/June 2003, doi:10.1109/TKDE.2003.1198389
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