"Independence is Good: Dependency-Based Histogram Synopses for High-Dimensional Data"
Minos Garofalakis, and
Proceedings of ACM SIGMOD'2001,
Santa Barbara, California, May 2001, pp. 199-210.
Approximating the joint data distribution of a multi-dimensional data
set through a compact and accurate histogram synopsis is a fundamental
problem arising in numerous practical scenarios, including query
optimization and approximate query answering.
Existing solutions either rely on simplistic independence assumptions
or try to directly approximate the full joint data distribution over
the complete set of attributes.
Unfortunately, both approaches are doomed to fail for high-dimensional
data sets with complex correlation patterns between attributes.
In this paper, we propose a novel approach to histogram-based synopses
that employs the solid foundation of statistical interaction models to
explicitly identify and exploit the statistical characteristics of the
Abstractly, our key idea is to break the synopsis into
(1) a statistical interaction model that accurately captures significant
correlation and independence patterns in data, and
(2) a collection of histograms on low-dimensional marginals that, based on
the model, can provide accurate approximations of the overall joint data
Extensive experimental results with several real-life data sets
verify the effectiveness of our approach.
An important aspect of our general, model-based methodology is that it can be
used to enhance the performance of other synopsis techniques that are based
on data-space partitioning (e.g., wavelets) by providing an effective tool
to deal with the "dimensionality curse".
Amol's talk slides
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