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EDBT
2011
ACM

Fast and accurate computation of equi-depth histograms over data streams

8 years 1 months ago
Fast and accurate computation of equi-depth histograms over data streams
Equi-depth histograms represent a fundamental synopsis widely used in both database and data stream applications, as they provide the cornerstone of many techniques such as query optimization, approximate query answering, distribution fitting, and parallel database partitioning. Equi-depth histograms try to partition a sequence of data in a way that every part has the same number of data items. In this paper, we present a new algorithm to estimate equi-depth histograms for high speed data streams over sliding windows. While many previous methods were based on quantile computations, we propose a new method called BAr Splitting Histogram (BASH) that provides an expected ϵ-approximate solution to compute the equi-depth histogram. Extensive experiments show that BASH is at least four times faster than one of the best existing approaches, while achieving similar or better accuracy and in some cases using less memory. The experimental results also indicate that BASH is more stable on data...
Hamid Mousavi, Carlo Zaniolo
Added 27 Aug 2011
Updated 27 Aug 2011
Type Journal
Year 2011
Where EDBT
Authors Hamid Mousavi, Carlo Zaniolo
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