Probabilistic graphical models and their role in databases

Probabilistic graphical models provide a framework for compact representation and efficient reasoning about the joint probability distribution of several interdependent variables. This is a classical topic with roots in statistical physics. In recent years, spurred by several applications in unstructured data integration, sensor networks, image processing, bio-informatics, and code design, the topic has received renewed interest in the machine learning, data mining, and database communities. Techniques from graphical models have also been applied to many topics directly of interest to the database community including information extraction, sensor data analysis, imprecise data representation and querying, selectivity estimation for query optimization, and data privacy. As database research continues to expand beyond the confines of traditional enterprise domains, we expect both the need and applicability of probabilistic graphical models to increase dramatically over the next few years. With this tutorial, we are aiming to provide a foundational overview of probabilistic graphical models to the database community, accompanied by a brief overview of some of the recent research literature on the role of graphical models in databases.