Abstract
Given a collection of data objects, the skyline problem is to select the objects which are not dominated by any others. In this paper, we propose a new variation of the skyline problem, called the combination skyline problem. The goal is to find the fixed-size combinations of objects which are skyline among all possible combinations. Our problem is technically challenging as traditional skyline approaches are inapplicable to handle a huge number of possible combinations. By indexing objects with an R-tree, our solution is based on object-selecting patterns that indicate the number of objects to be selected for each MBR. We develop two major pruning conditions to avoid unnecessary expansions and enumerations, as well as a technique to reduce space consumption on storing the skyline for each rule in the object-selecting pattern. The efficiency of the proposed algorithm is demonstrated by extensive experiments on both real and synthetic datasets.
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Guo, X., Xiao, C., Ishikawa, Y. (2012). Combination Skyline Queries. In: Hameurlain, A., Küng, J., Wagner, R., Liddle, S.W., Schewe, KD., Zhou, X. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems VI. Lecture Notes in Computer Science, vol 7600. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34179-3_1
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DOI: https://doi.org/10.1007/978-3-642-34179-3_1
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