BayesStore: managing large, uncertain data repositories with probabilistic graphical models

Several real-world applications need to effectively manage and reason about large amounts of data that are inherently uncertain. For instance, pervasive computing applications must constantly reason about volumes of noisy sensory readings for a variety of reasons, including motion prediction and human behavior modeling. Such probabilistic data analyses require sophisticated machine-learning tools that can effectively model the complex spatio/temporal correlation patterns present in uncertain sensory data. Unfortunately, to date, most existing approaches to probabilistic database systems have relied on somewhat simplistic models of uncertainty that can be easily mapped onto existing relational architectures: Probabilistic information is typically associated with individual data tuples, with only limited or no support for effectively capturing and reasoning about complex data correlations. In this paper, we introduce BAYESSTORE, a novel probabilistic data management architecture built o...

• Type: Journal
• Year: 2008
• Where: PVLDB
• Authors: Daisy Zhe Wang, Eirinaios Michelakis, Minos N. Garofalakis, Joseph M. Hellerstein
• Keywords: Probabilistic, Probabilistic Data, PVLDB 2008, Statistical Model