In this paper we propose to use data mining techniques for database reverse engineering process. A crucial problem in this process concerns the discovery of similarity between attributes before constructing the conceptual model. The essence of our approach is to mine user queries collected on the database in order to extract specific similarity measure that we call distance between 2 attributes. Indeed most database reverse engineering methods are based on the observation of several sources which generally are the existing database schema, the data themselves and application programs including queries. Unlike previous propositions which analyze only the structure of joins in queries, the main idea of this paper is to exploit the large volume of information stored in queries in order to extract some semantic properties on attributes. Thus we propose to apply a data mining algorithm on a query base collected on the data. The objective is to extract semantic links that do not appear obviously in the schema or in the data and are suggested implicitly by expert users in their queries. In this paper, we focus mainly on the problem of attribute similarity which is quite important in database reverse engineering. We describe a method by which similarities between attributes are discovered according to context measures without taking into consideration the naming policy used by database designers.

Date Published: 12 March 2002
PDF: 8 pages
Proc. SPIE 4730, Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV, (12 March 2002); doi: 10.1117/12.460226

Published in SPIE Proceedings Vol. 4730:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV
Belur V. Dasarathy, Editor(s)