Queries and materialized views on probabilistic databases

We review in this paper some recent yet fundamental results on evaluating queries over probabilistic databases. While one can see this problem as a special instance of general purpose probabilistic inference, we describe in this paper two key database specific techniques that significantly reduce the complexity of query evaluation on probabilistic databases. The first is the separation of the query and the data: we show here that by doing so, one can identify queries whose data complexity is #P-hard, and queries whose data complexity is in PTIME. The second is the aggressive use of previously computed query results (materialized views): in particular, by rewriting a query in terms of views, one can reduce its complexity from #P-complete to PTIME. We describe a notion of a partial representation for views, show how to validated it based on the view definition, then show how to use it during query evaluation.