Random Databases with Approximate Record Matching

Oleg Seleznjev () and Bernhard Thalheim
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Oleg Seleznjev: Umeå University
Bernhard Thalheim: Christian-Albrechts University

Methodology and Computing in Applied Probability, 2010, vol. 12, issue 1, 63-89

Abstract: Abstract In many database applications in telecommunication, environmental and health sciences, bioinformatics, physics, and econometrics, real-world data are uncertain and subjected to errors. These data are processed, transmitted and stored in large databases. We consider stochastic modelling for databases with uncertain data and for some basic database operations (for example, join, selection) with exact and approximate matching. Approximate join is used for merging or data deduplication in large databases. Distribution and mean of the join sizes are studied for random databases. A random database is treated as a table with independent random records with a common distribution (or a set of random tables). These results can be used for integration of information from different databases, multiple join optimization, and various probabilistic algorithms for structured random data.

Keywords: Random database; Join; Tests; Approximate matching; Rényi entropy; Poisson approximation; Primary 62E20, 94A17; Secondary 68P15 (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1007/s11009-008-9092-4

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