Indexing correlated probabilistic databases
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Continuously monitoring top-k uncertain data streams: a probabilistic threshold method
Distributed and Parallel Databases
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
PODS: a new model and processing algorithms for uncertain data streams
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Lineage processing over correlated probabilistic databases
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
A generic framework for handling uncertain data with local correlations
Proceedings of the VLDB Endowment
A unified approach to ranking in probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
Probabilistic management of OCR data using an RDBMS
Proceedings of the VLDB Endowment
CLARO: modeling and processing uncertain data streams
The VLDB Journal — The International Journal on Very Large Data Bases
Xtream: a system for continuous querying over uncertain data streams
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
Approximation trade-offs in a Markovian stream warehouse: An empirical study
Information Systems
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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 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. We also present an algorithm for operator ordering that judiciously rearranges the query operators to make the query evaluation tractable, if possible given the query. Our extensive experimental evaluation illustrates the advantages of exploiting the structured nature of correlations in probabilistic streams.