A Unified Approach to Ranking in Probabilistic Databases

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 {\em 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 {\em parameterized ranking functions}, called \PRFs\ and \PRFe, that generalize or can approximate many of the previously proposed ranking functions. We present novel {\em generating functions}-based algorithms for efficiently ranking large datasets according to these ranking functions, even if the datasets exhibit complex correlations modeled using {\em probabilistic and/xor trees} or {\em 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 \PRFe, at approximating other ranking functions and the scalability of our proposed algorithms for exact or approximate ranking.

(joint work with Barna Saha and Amol Deshpande)