" Discovering ER models from Legacy Relational Databases"

6/6/1997: [time not recorded]
[location not recorded]

Abstract: A conceptual model of a database is a specification of objects, attributes, and their relationships contained in the database. Although understanding such a model is a crucial step in many applications, obtaining it from legacy databases is a challenging task. A given database may have missing schema information (such as keys and foreign keys) and contain noisy data. In this talk, we will present a method to discover a conceptual model from a relational database and represent it in an Entity-Relationship(ER) model. The main ideas are: discovering characteristics of data that are critical for model building, filtering out the irrelevant characteristics caused by noisy data, and generating ER model by reversing the well-understood process of converting a ER model to a relational database. Our goal is to help users to recover and understand the conceptual data model of a large and even badly designed database, so that further data processing tasks (such as data mining or database integration) can be performed. This method is implemented in LDL (Logical Definition Language) and C++, and it is tested on some man-made databases. We will analyse the results of our experimentation and discuss related work and future research directions. Xuejun Wang is the recipient of an ISI Graduate Fellowship.

Last updated: Mon Jun 19 17:44:06 2006