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PRS: efficient range skyline computation on massive data via presorting

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Abstract

In many applications, range skyline query is an important operation to find the interesting tuples in a potentially huge data space. Given selection condition, range skyline query returns tuples both satisfying the specified selection condition and not dominated by other satisfying tuples. It is found that most of the existing skyline algorithms do not consider the selection condition. This paper proposes a novel table-scan-based Presorted-table-based Range Skyline (PRS) algorithm to efficiently compute range skyline results on massive data. PRS first presorts the table for early termination. The early termination checking is proposed in this paper, along with the theoretical analysis of scan depth. The selection checking and dominance checking are devised in this paper to skip the unsatisfying or dominated tuples directly. The theoretical analysis proves that the overwhelming majority of candidates can be skipped. The extensive experimental results, conducted on synthetic and real-life data sets, show that PRS outperforms the existing algorithms significantly.

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Notes

  1. In this paper, the tuples are considered in general position [31].

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61872106, 61632010, 61502121 and 61402130, National key research and development program of China under Grant No. 2016YFB1000703 and Weihai-HIT co-construction program under Grant No. ZMZ001702.

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  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

    Xixian Han, Xue Li, Bailing Wang & Hong Gao

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  1. Xixian Han
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  2. Xue Li
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  3. Bailing Wang
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  4. Hong Gao
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Correspondence to Xixian Han.

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Han, X., Li, X., Wang, B. et al. PRS: efficient range skyline computation on massive data via presorting. Knowl Inf Syst 60, 1511–1548 (2019). https://doi.org/10.1007/s10115-018-1310-y

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  • DOI: https://doi.org/10.1007/s10115-018-1310-y

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