GRN model of probabilistic databases: construction, transition and querying

Ruiwen Chen, Yongyi Mao, Iluju Kiringa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Under the tuple-level uncertainty paradigm, we formalize the use of a novel graphical model, Generator-Recognizer Network (GRN), as a model of probabilistic databases. The GRN modeling framework is capable of representing a much wider range of tuple dependency structure. We show that a GRN representation of a probabilistic database may undergo transitions induced by imposing constraints or evaluating queries. We formalize procedures for these two types of transitions such that the resulting graphical models after transitions remain as GRNs. This formalism makes GRN a self-contained modeling framework and a closed representation system for probabilistic databases - a property that is lacking in most existing models. In addition, we show that exploiting the transitional mechanisms allows a systematic approach to constructing GRNs for arbitrary probabilistic data at arbitrary stages. Advantages of GRNs in query evaluation are also demonstrated.
Original languageEnglish
Title of host publicationProceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2010, Indianapolis, Indiana, USA, June 6-10, 2010
PublisherACM
Pages291-302
Number of pages12
ISBN (Print)978-1-4503-0032-2
DOIs
Publication statusPublished - 2010

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