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Uncertain data streams, where data is incomplete, imprecise, and even misleading, have been observed in many environments. Feeding such data streams to existing stream systems produces results of unknown quality, which is of paramount concern to monitoring applications. In this paper, we present the PODS system that supports stream processing for uncertain data naturally captured using continuous random variables. PODS employs a unique data model that is flexible and allows efficient
doi:10.1145/1807167.1807187
dblp:conf/sigmod/TranPLDL10
fatcat:pfonw4lck5hrlmu5n7tam4mgny