  We address the problem of finding a "best" deterministic query answer to a
query over a probabilistic database. For this purpose, we propose the notion of
a consensus world (or a consensus answer) which is a deterministic world
(answer) that minimizes the expected distance to the possible worlds (answers).
This problem can be seen as a generalization of the well-studied inconsistent
information aggregation problems (e.g. rank aggregation) to probabilistic
databases. We consider this problem for various types of queries including SPJ
queries, \Topk queries, group-by aggregate queries, and clustering. For
different distance metrics, we obtain polynomial time optimal or approximation
algorithms for computing the consensus answers (or prove NP-hardness). Most of
our results are for a general probabilistic database model, called {\em and/xor
tree model}, which significantly generalizes previous probabilistic database
models like x-tuples and block-independent disjoint models, and is of
independent interest.
