GRN model of probabilistic databases: construction, transition and querying

Authors:
Ruiwen Chen;Yongyi Mao;Iluju Kiringa
Affiliations:
University of Ottawa, Ottawa, ON, Canada;University of Ottawa, Ottawa, ON, Canada;University of Ottawa, Ottawa, ON, Canada
Venue:
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Year:
2010

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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.