Abstract
Integration of multiple heterogeneous data sources continues to be a critical problem for many application domains and a challenge for researchers world-wide. Schema matching, a fundamental aspect of integration, has been a well-studied problem. However researchers have, for the most part, concentrated on the development of different schema matching algorithms, and their performance with respect to the number of matches produced. To the best of our knowledge, current research in schema matching does not address the issue of quality of matching. We believe that quality of match is an important measure that can not only provide a basis for comparing multiple matches, but can also be used as a metric to compare as well as optimize existing match algorithms. In this paper, we define the Quality of Match (QoM) metric, and provide qualitative and quantitative analysis techniques to evaluate the QoM of two given schemata. In particular, we introduce a taxonomy of schema matches as a qualitative analysis technique, and a weight-based match model that in concert with the taxonomy provides a quantitative measure of the QoM. We show, via examples, how QoM can be used to distinguish the “goodness” of one match in comparison with other matches.
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Tansalarak, N., Claypool, K. (2003). QoM: Qualitative and Quantitative Schema Match Measure. In: Song, IY., Liddle, S.W., Ling, TW., Scheuermann, P. (eds) Conceptual Modeling - ER 2003. ER 2003. Lecture Notes in Computer Science, vol 2813. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39648-2_6
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DOI: https://doi.org/10.1007/978-3-540-39648-2_6
Publisher Name: Springer, Berlin, Heidelberg
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