  The dramatic growth in the number of application domains that naturally
generate probabilistic, uncertain data has resulted in a need for efficiently
supporting complex querying and decision-making over such data. In this paper,
we present a unified approach to ranking and top-k query processing in
probabilistic databases by viewing it as a multi-criteria optimization problem,
and by deriving a set of features that capture the key properties of a
probabilistic dataset that dictate the ranked result. We contend that a single,
specific ranking function may not suffice for probabilistic databases, and we
instead propose two parameterized ranking functions, called PRF-w and PRF-e,
that generalize or can approximate many of the previously proposed ranking
functions. We present novel generating functions-based algorithms for
efficiently ranking large datasets according to these ranking functions, even
if the datasets exhibit complex correlations modeled using probabilistic
and/xor trees or Markov networks. We further propose that the parameters of the
ranking function be learned from user preferences, and we develop an approach
to learn those parameters. Finally, we present a comprehensive experimental
study that illustrates the effectiveness of our parameterized ranking
functions, especially PRF-e, at approximating other ranking functions and the
scalability of our proposed algorithms for exact or approximate ranking.
