Samples for Understanding Data-Semantics in Relations

Authors:
Fabien De Marchi;Stéphane Lopes;Jean-Marc Petit
Affiliations:
-;-;-
Venue:
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Year:
2002

Citing 10
Cited 4

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A Guided Tour of Relational Databases and Beyond

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A Framework for Understanding Existing Databases

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Analysis of existing databases at the logical level: the DBA companion project

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Semantic sampling of existing databases through informative Armstrong databases

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Empirical evidence for the usefulness of Armstrong relations in the acquisition of meaningful functional dependencies

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Using data samples in validating data models

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Abstract

From statistics, sampling technics were proposed and some of them were proved to be very useful in many database applications. Rather surprisingly, it seems these works never consider the preservation of data semantics. Since functional dependencies (FDs) are known to convey most of data semantics, an interesting issue would be to construct samples preserving FDs satisfied in existing relations.To cope with this issue, we propose in this paper to define Informative Armstrong Relations (IARs); a relation s is an IAR for a relation r if s is a subset of r and if FDs satisfied in s are exactly the same as FDs satisfied in r. Such a relation always exists since r is obviously an IAR for itself; moreover we shall point out that small IARs with interesting bounded sizes exist. Experiments on relations available in the KDD archive were conducted and highlight the interest of IARs to sample existing relations.