A compositional framework for complex queries over uncertain data

The ability to flexibly compose confidence computation with the operations of relational algebra is an important feature of probabilistic database query languages. Computing confidences is computationally hard, however, and has to be approximated in practice. In a compositional query language, even very small errors caused by approximation can lead to an entirely incorrect result: A selection operation on an approximated probability can incorrectly keep or drop a tuple even if the probability value has been approximated to a very narrow confidence interval. In this paper, we study the query evaluation problem for compositional query languages for probabilistic databases with particular focus on providing overall result quality guarantees in the face of approximate intermediate results. We present a framework for evaluating compositional queries based on a new representation system that can capture uncertainty about probabilities. More specifically, we consider probability intervals in...

• Type: Conference
• Year: 2009
• Where: ICDT
• Authors: Christoph Koch, Michaela Götz
• Keywords: Compositional Query Languages, Database, ICDT 2009, Probabilistic Database Query, Relational Algebra Plan