On Pruning for Top-K Ranking in Uncertain Databases

Top-k ranking for an uncertain database is to rank tuples in it so that the best k of them can be determined. The problem has been formalized under the unified approach based on parameterized ranking functions (PRFs) and the possible world semantics. Given a PRF, one can always compute the ranking function values of all the tuples to determine the top-k tuples, which is a formidable task for large databases. In this paper, we present a general approach to pruning for the framework based on PRFs. We show a mathematical manipulation of possible worlds which reveals key insights in the part of computation that may be pruned and how to achieve it in a systematic fashion. This leads to concrete pruning methods for a wide range of ranking functions. We show experimentally the effectiveness of our approach.