Optimizing mpf queries: decision support and probabilistic inference

Managing uncertain data using probabilistic frameworks has attracted much interest lately in the database literature, and a central computational challenge is probabilistic inference. This paper presents a broad class of aggregate queries, called MPF queries, inspired by the literature on probabilistic inference in statistics and machine learning. An MPF (Marginalize a Product Function) query is an aggregate query over a stylized join of several relations. In probabilistic inference, this join corresponds to taking the product of several probability distributions, while the aggregate operation corresponds to marginalization. Probabilistic inference can be expressed directly as MPF queries in a relational setting, and therefore, by optimizing evaluation of MPF queries, we provide scalable support for probabilistic inference in database systems. To optimize MPF queries, we build on ideas from database query optimization as well as traditional algorithms such as Variable Elimination and ...

• Type: Conference
• Year: 2007
• Where: SIGMOD
• Authors: Héctor Corrada Bravo, Raghu Ramakrishnan
• Keywords: Database, MPF Queries, Probabilistic Inference, Probabilistic Inference Literature, SIGMOD 2007