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Computing Exact Skyline Probabilities for Uncertain Databases
Dec. 2012 (vol. 24 no. 12)
pp. 2113-2126
Dongwon Kim, Pohang University of Science and Technology (POSTECH)
Hyeonseung Im, Pohang University of Science and Technology (POSTECH)
Sungwoo Park, Pohang University of Science and Technology (POSTECH)
With the rapid increase in the amount of uncertain data available, probabilistic skyline computation on uncertain databases has become an important research topic. Previous work on probabilistic skyline computation, however, only identifies those objects whose skyline probabilities are higher than a given threshold, or is useful only for 2D data sets. In this paper, we develop a probabilistic skyline algorithm called PSkyline which computes exact skyline probabilities of all objects in a given uncertain data set. PSkyline aims to identify blocks of instances with skyline probability zero, and more importantly, to find incomparable groups of instances and dispense with unnecessary dominance tests altogether. To increase the chance of finding such blocks and groups of instances, PSkyline uses a new in-memory tree structure called Z-tree. We also develop an online probabilistic skyline algorithm called O-PSkyline for uncertain data streams and a top-k probabilistic skyline algorithm called K-PSkyline to find top-k objects with the highest skyline probabilities. Experimental results show that all the proposed algorithms scale well to large and high-dimensional uncertain databases.
Index Terms:
Probabilistic logic,Probability distribution,Mathematical model,Equations,Query processing,Upper bound,data stream,Skyline computation,skyline probability,uncertain database
Citation:
Dongwon Kim, Hyeonseung Im, Sungwoo Park, "Computing Exact Skyline Probabilities for Uncertain Databases," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 12, pp. 2113-2126, Dec. 2012, doi:10.1109/TKDE.2011.164
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