Performance analysis on ranked queries in uncertain databases

Top-k processing in Uncertain Databases is semantically and computationally different from traditional top-k processing. The interplay between score and uncertainty information makes traditional top-k processing techniques inapplicable to uncertain databases. The existing approaches are all based on the assumption that the underlying data are exact (or certain). We construct a framework that encapsulates a novel probabilistic model and efficient query processing techniques to tackle the challenges raised by uncertain data settings. We introduce two effective pruning methods, spatial and probabilistic pruning, to help reduce the PRank search space. A special case of PRank with linear preference functions is also studied. Then, we seamlessly integrate these pruning heuristics into the PRank query procedure. And We would have propose and tackle the PRank query processing over the join of two distinct uncertain databases by means of J-Prank Query Processing. We provide efficient solutions to compute this ranking across the major models of uncertain data, such as attribute-level and tuple-level uncertainty. For an uncertain relation of N tuples, the processing cost is O(N log N)-no worse than simply sorting the relation. To demonstrate the efficiency and effectiveness of our proposed approaches in answering PRank queries, extensive experiments have been done in terms of both wall clock time and the number of candidates to be refined.