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Histograms based on the minimum description length principle

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Abstract

Histograms have been widely used for selectivity estimation in query optimization, as well as for fast approximate query answering in many OLAP, data mining, and data visualization applications. This paper presents a new family of histograms, the Hierarchical Model Fitting (HMF) histograms, based on the Minimum Description Length principle. Rather than having each bucket of a histogram described by the same type of model, the HMF histograms employ a local optimal model for each bucket. The improved effectiveness of the locally chosen models offsets more than the overhead of keeping track of the representation of each individual bucket. Through a set of experiments, we show that the HMF histograms are capable of providing more accurate approximations than previously proposed techniques for many real and synthetic data sets across a variety of query workloads.

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Authors and Affiliations

  1. Sobey School of Business, Saint Mary’s University, Halifax, NS, B3H 3C3, Canada

    Hai Wang

  2. Department of Computer Science, University of Toronto, Toronto, ON, M5S 3G4, Canada

    Kenneth C. Sevcik

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  1. Hai Wang
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  2. Kenneth C. Sevcik
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Correspondence to Hai Wang.

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Wang, H., Sevcik, K.C. Histograms based on the minimum description length principle. The VLDB Journal 17, 419–442 (2008). https://doi.org/10.1007/s00778-006-0015-0

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