"Learning Statistical Models from Relational Data"

12/21/2001: [time not recorded]
[location not recorded]

Abstract: A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat" data representations. Thus, to apply these methods, we are forced to convert the data into a flat form, thereby losing much of the relational structure present in the data and potentially introducing statistical skew. These drawbacks severely limit the ability of current methods to mine relational databases. In this talk I will review recent work on probabilistic models, including Bayesian networks (BNs) and Probabilistic Relational Models (PRMs), and then describe the development of techniques for automatically inducing PRMs directly from structured data stored in a relational or object-oriented database. These algorithms provide the necessary tools to discover patterns in structured data, and provide new techniques for mining relational data. As we go along, I'll present experimental results in several domains, including a biological domain describing tuberculosis epidemiology, a database of scientific paper author and citation information, and Web data. Finally I will present an application of these techniques to the task of selectivity estimation for database query optimization.

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