PIP: A Database System for Great and Small Expectations

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PIP: A Database System for Great and Small Expectations
Estimation via sampling out of highly selective join queries is well known to be problematic, most notably in online aggregation. Without goal-directed sampling strategies, samples falling outside of the selection constraints lower estimation efficiency at best, and cause inaccurate estimates at worst. This problem appears in general probabilistic database systems, where query processing is tightly coupled with sampling. By committing to a set of samples before evaluating the query, the engine wastes effort on samples that will be discarded, query processing that may need to be repeated, or unnecessarily large numbers of samples. We describe PIP, a general probabilistic database system that uses symbolic representations of probabilistic data to defer computation of expectations, moments, and other statistical measures until the expression to be measured is fully known. This approach is sufficiently general to admit both continuous and discrete distributions. Moreover, deferring samplin...
Oliver Kennedy, Christoph Koch
Added 20 Dec 2009
Updated 03 Jan 2010
Type Conference
Year 2010
Where ICDE
Authors Oliver Kennedy, Christoph Koch
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