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.
Keywords
- Functional Dependency
- Horn Clause
- Data Semantic
- Initial Relation
- Extend Database Technology
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This work is partially supported by the AS CNRS-STIC “Data Mining”
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De Marchi, F., Lopes, S., Petit, JM. (2002). Samples for Understanding Data-Semantics in Relations. In: Hacid, MS., Raś, Z.W., Zighed, D.A., Kodratoff, Y. (eds) Foundations of Intelligent Systems. ISMIS 2002. Lecture Notes in Computer Science(), vol 2366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48050-1_60
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DOI: https://doi.org/10.1007/3-540-48050-1_60
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