Query Aware Determinization of Uncertain Objects
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Abstract
The main aim of this paper is to think about the trouble of determining probabilistic data to allow such data to be stored in legacy systems that agree only deterministic input. Probabilistic data may be produced by mechanized data analysis methods such as entity resolution, information extraction, and speech processing etc. The target is to make a deterministic depiction of probabilistic data that optimizes the excellence of the end-application built on deterministic data. We discover such a determinization problem in the background of two dissimilar data processing jobs – selection and triggers queries. Here approaches such as thresholding or top-1 selection usually used for determinization lead to suboptimal presentation for such applications. As an alternative, we expand a query-aware strategy and demonstrate its rewards over existing solutions through a complete empirical evaluation over real and synthetic datasets.
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