"Approximate Query Processing Using Wavelets"

by Kaushik Chakrabarti, Minos Garofalakis, Rajeev Rastogi, and Kyuseok Shim.
Proceedings of VLDB'2000, Cairo, Egypt, September 2000, pp. 111-122.
[** Invited to the "Best of VLDB'2000" Special Issue of The VLDB Journal **]


Approximate query processing has emerged as a cost-effective approach for dealing with the huge data volumes and stringent response-time requirements of today's decision-support systems. Most work in this area, however, has so far been limited in its applicability and query processing scope. In this paper, we propose the use of multi-dimensional wavelets as an effective tool for general-purpose approximate query processing in modern, high-dimensional applications. Our approach is based on building wavelet-coefficient synopses of the data and using these synopses to provide approximate answers to queries. We develop novel query processing algorithms that operate directly on the wavelet-coefficient synopses of relational tables, allowing us to process arbitrarily complex queries entirely in the wavelet-coefficient domain. This, of course, guarantees extremely fast response times since our approximate query execution engine can do the bulk of its processing over compact sets of wavelet coefficients, essentially postponing the expansion into relational tuples until the end-result of the query. An extensive experimental study with synthetic as well as real-life data sets establishes the effectiveness of our wavelet-based approach compared to sampling and histograms.

[ camera-ready paper (pdf) (ps.gz) | journal version (in The VLDB Journal) | Kaushik's talk slides (ppt.gz) ]

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