Efficient Query Evaluation over Temporally Correlated Probabilistic Streams

Many real world applications such as sensor networks and other monitoring applications naturally generate probabilistic streams that are highly correlated in both time and space. Query processing over such streaming data must be cognizant of these correlations, since they can significantly alter the final query results. Several prior works have suggested approaches to handling correlations in probabilistic databases. However those approaches are either unable to represent the types of correlations that probabilistic streams exhibit, or can not be applied directly to our problem because of their complexity. In this paper, we develop a system for managing and querying such streams by exploiting the fact that most real-world probabilistic streams exhibit highly structured Markovian correlations. Our approach is based on the previously proposed framework of viewing probabilistic query evaluation as inference over graphical models; we show how to efficiently construct graphical models for the common stream processing operators, and how to efficiently perform inference over them in an incremental fashion. Our extensive experimental evaluation illustrates the advantages of exploiting the structured nature of correlations in probabilistic streams.