Approximating sliding windows by cyclic tree-like histograms for efficient range queries
Data & Knowledge Engineering
Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches
Foundations and Trends in Databases
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Data reduction is a basic step in a KDD process useful for delivering to successive stages more concise and meaningful data. When mining is applied to data streams, that are continuous data flows, the issue of suitably reducing them is highly interesting, in order to arrange effective approaches requiring multiple scans on data, that, in such a way, may be performed over one or more reduced sliding windows. A class of queries, whose importance in the context of KDD is widely accepted, corresponds to sum range queries. In this paper we propose a histogram-based technique for reducing sliding windows supporting approximate arbitrary (i.e., non biased) sum range queries. The histogram, based on a hierarchical structure (opposed to the flat structure of traditional ones), results suitable for directly supporting hierarchical queries, and, thus, drill-down and roll-up operations. In addition, the structure well supports sliding window shifting and quick query answering (both these operations are loarithmic in the sliding window size). Experimental analysis shows the superiority of our method in terms of accuracy w.r.t. the state-of-the-art approaches in the context of histogram-based sliding window reduction techniques.