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An Adaptive Parallel PI-Skyline Query for Probabilistic and Incomplete Database

In the context of large quantities of information, the skyline query is a particularly useful tool for data mining and decision-making. However, the massive amounts of information on the Internet are frequently incomplete and uncertain due to data randomness, transmission errors, and many other reasons. Therefore, an efficient skyline query algorithm over an incomplete uncertain database is imperative. To address this issue, this paper proposes an efficient algorithm to apply skyline query on probabilistic incomplete data. The algorithm is based on U-Skyline model to avoid disadvantages of traditional P-Skyline model. The proposed methods introduce some novel concepts including transferred tuples, leading tuples and the new dominance relationship between probabilistic incomplete data. Besides, it is a parallel processing algorithm. Extensive experiments demonstrate the effectiveness and efficiency of the proposed algorithms.