Abstract
Uncertain data is inherent in a few important applications. It is far from trivial to extend ranking queries (also known as top-k queries), a popular type of queries on certain data, to uncertain data. In this paper, we cast ranking queries on uncertain data using three parameters: rank threshold k, probability threshold p, and answer set size threshold l. Systematically, we identify four types of ranking queries on uncertain data. First, a probability threshold top-k query computes the uncertain records taking a probability of at least p to be in the top-k list. Second, a top-(k, l) query returns the top-l uncertain records whose probabilities of being ranked among top-k are the largest. Third, the p-rank of an uncertain record is the smallest number k such that the record takes a probability of at least p to be ranked in the top-k list. A rank threshold top-k query retrieves the records whose p-ranks are at most k. Last, a top-(p, l) query returns the top-l uncertain records with the smallest p-ranks. To answer such ranking queries, we present an efficient exact algorithm, a fast sampling algorithm, and a Poisson approximation-based algorithm. To answer top-(k, l) queries and top-(p, l) queries, we propose PRist+, a compact index. An efficient index construction algorithm and efficacious query answering methods are developed for PRist+. An empirical study using real and synthetic data sets verifies the effectiveness of the probabilistic ranking queries and the efficiency of our methods.
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The authors are grateful to the anonymous reviewers and Dr. Michael Böhlen, the associate editor, for their constructive and insightful advice on the paper. This research was supported by an NSERC Discovery Grant and an NSERC Discovery Accelerator Supplement Grant. All opinions, findings, conclusions and recommendations in this paper are those of the authors and do not necessarily reflect the views of the funding agencies.
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Hua, M., Pei, J. & Lin, X. Ranking queries on uncertain data. The VLDB Journal 20, 129–153 (2011). https://doi.org/10.1007/s00778-010-0196-4
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DOI: https://doi.org/10.1007/s00778-010-0196-4