On probabilistic fixpoint and Markov chain query languages

Abstract

We study highly expressive query languages such as datalog, fixpoint, and while-languages on probabilistic databases. We generalize these languages such that computation steps (e.g. datalog rules) can fire probabilistically. We define two possible semantics for such query languages, namely inflationary semantics where the results of each computation step are added to the current database and noninflationary queries that induce a random walk in-between database instances. We then study the complexity of exact and approximate query evaluation under these semantics.