An algebra for probabilistic databases

Author

Pittarelli, Michael

Author_Institution

SUNY Inst. of Technol., Utica, NY, USA

Volume

6

Issue

2

fYear

1994

fDate

4/1/1994 12:00:00 AM

Firstpage

293

Lastpage

303

Abstract

An algebra is presented for a simple probabilistic data model that may be regarded as an extension of the standard relational model. The probabilistic algebra is developed in such a way that (restricted to α-acyclic database schemes) the relational algebra is a homomorphic image of it. Strictly probabilistic results are emphasized. Variations on the basic probabilistic data model are discussed. The algebra is used to explicate a commonly used statistical smoothing procedure and is shown to be potentially very useful for decision support with uncertain information

Keywords

Bayes methods; Markov processes; algebra; data structures; database management systems; database theory; decision support systems; probability; relational algebra; uncertainty handling; α-acyclic database schemes; Bayes networks; Markov networks; decision support; homomorphic image; probabilistic algebra; probabilistic data model; probabilistic databases; relational algebra; statistical smoothing procedure; uncertain information; Algebra; Data models; Image databases; Image reconstruction; Markov random fields; Power system modeling; Probability; Relational databases; Smoothing methods; Transaction databases;

fLanguage

English

Journal_Title

Knowledge and Data Engineering, IEEE Transactions on

Publisher

ieee

ISSN

1041-4347

Type

jour

DOI

10.1109/69.277772

Filename

277772