Abstract
Over the past years, the skyline query has already caused wide attention in database community. For the skyline computation over incomplete data, the existing algorithms focus mainly on reducing the dominance tests among these points with the same bitmap representation by exploiting \( Bucket \) technique. While, the issue of exhaustive comparisons among those points in different buckets remains unsolved, which is the major cost. In this paper, we present a general framework COBO for skyline computation over incomplete data. And based on COBO, we develop an efficient algorithm ISSA in two phases: \( pruning \) \( compared \) \( list \) and \( reducing \) \( expected \) \( comparison \) \( times \). We construct a compared list order according to ACD to diminish significantly the total comparisons among the points in different buckets. The experimental evaluation on synthetic and real data sets indicates that our algorithm outperforms existing state-of-the-art algorithm 1 to 2 orders of magnitude in comparisons.
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Zhang, K., Gao, H., Wang, H., Li, J. (2016). ISSA: Efficient Skyline Computation for Incomplete Data. In: Gao, H., Kim, J., Sakurai, Y. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9645. Springer, Cham. https://doi.org/10.1007/978-3-319-32055-7_26
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DOI: https://doi.org/10.1007/978-3-319-32055-7_26
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