Constructing optimal wavelet synopses

Abstract

The wavelet decomposition is a proven tool for constructing concise synopses of massive data sets and rapid changing data streams, which can be used to obtain fast approximate, with accuracy guarantees, answers. In this work we present a generic formulation for the problem of constructing optimal wavelet synopses under space constraints for various error metrics, both for static and streaming data sets. We explicitly associate existing work and categorize it according to the previous problem formulation and, further, we present our current work and identify its contributions in this context. Various interesting open problems are described and our future work directions are clearly stated. © Springer-Verlag Berlin Heidelberg 2006.