OVERVIEW

Consistency and completeness are considered two important dimensions of data quality [ ^[1], ^[9], ^[20]]. However, they are usually compromised by errors, accidentally or intentionally introduced, in a database system. Such errors result in inconsistent, incomplete, and erroneous data elements. A small variation in the representation of a data object may, for example, produce a unique instantiation of the object being represented. In order to improve the accuracy of the data stored in a database system, the data must be compared either with real-world counterparts or with other data, stored in the same or in a different system.

This chapter has been written by Vassilios S. Verykios at Drexel University under the direction of Ahmed Elmagarmid at Purdue University. It profiles one aspect of the on-going Purdue research projects. Specifically, it addresses the problem of matching records, which refer to the same entity, by computing their similarity. In this context, exact record matching has limited applicability since, even simple errors, like character transpositions, cannot be captured in the record linking process. The methodology deploys advanced data mining techniques for dealing with the high computational and inferential complexity of the approximate record matching process.