Ranking and aggregation queries are widely exploited in data exploration, data analysis and decision making scenarios. While most of the currently proposed ranking and aggregation techniques focus on deterministic data, several emerging applications involve data that is unclean or uncertain. Ranking and aggregating uncertain (probabilistic) data raises new challenges with respect to query semantics and processing, which makes conventional methods inapplicable. 

In this talk, I will introduce new formulations for ranking and aggregation queries in probabilistic databases. The new formulations are based on marriage of traditional ranking and aggregation algorithms with possible worlds semantics. In the light of these formulations, I will describe a generic processing framework supporting both query types, and leveraging existing query processing and indexing capabilities in current database systems. The framework encapsulates a state space model, and efficient search algorithms that compute query answers with optimality guarantees.