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سند علمی

Top-k Best Probability Queries on Probabilistic Data

Trieu Minh Nhut Le, Jinli Cao

چکیده

There has been much interest in answering top-k queries on probabilistic data in various applications such as market analysis, personalised services, and decision making. In relation to probabilistic data, the most common problem in answering top-k queries is selecting the semantics of results according to their scores and top-k probabilities. In this paper, we propose a novel top-k best probability query to obtain results which are not only the best top-k scores but also the best topk probabilities. We also introduce an efficient algorithm for top-k best probability queries without requiring the user’s defined threshold. Then, the top-k best probability answer is analysed, which satisfies the semantic ranking properties of queries [3, 18] on uncertain data. The experimental studies are tested with both the real data to verify the effectiveness of the top-k best probability queries and the efficiency of our algorithm.

اسناد مشابه

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