Continuous Uncertainty in Trio
MUD(2009)
摘要
We present extensions to Trio for incorporating continuous uncertainty into the system. Data items with uncertain possible values drawn from a continuous domain are represented through a generic set of functions. Our approach enables precise and ecient representation of arbitrary probability distribution functions, along with standard distri- butions such as Gaussians. We also describe how queries are processed eciently over this representation, without knowledge of specic distri- butions. For queries that cannot be answered exactly, we can provide approximate answers using sampling or histogram approximations, of- fering the user a cost-precision trade-o. Our approach exploits Trio's lineage and condence features, with smooth integration into the overall data model and system.
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关键词
probability distribution function,data model
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