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TDEP: efficiently processing top-k dominating query on massive data

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Abstract

In many applications including multicriteria decision making, top-k dominating query is a practically useful tool to return k tuples with the highest domination scores in a potentially huge data space. The existing algorithms, either requiring indexes built on the specific attribute subset, or incurring high I/O cost or memory cost, cannot process top-k dominating query on massive data efficiently. In this paper, a novel algorithm TDEP is proposed to utilize sorted lists built for each attribute with low cost to return top-k dominating result on massive data efficiently. Through analysis, it is found that TDEP can be divided into two phases: growing phase and shrinking phase. In each phase, TDEP retrieves the sorted lists in round-robin fashion and maintains the candidates until the stop condition is satisfied. The theoretical analysis is provided for the execution behavior in two phases. An efficient method is developed to compute the domination scores of tuples with the obtained candidates only. Besides, TDEP adopts early pruning to reduce the number of candidate tuples maintained significantly. The extensive experimental results, conducted on synthetic and real-life data sets, show the significant performance advantage of TDEP over the existing algorithms.

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Notes

  1. In this paper, we consider that the tuples are in general position [24].

  2. In the following section, we will see that the practical probability is even much higher.

  3. http://basketballreference.com/.

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Acknowledgments

We thank anonymous reviewers for their very useful comments and suggestions. This work was supported in part by the National Basic Research (973) Program of China under Grant No. 2012CB316200, the National Natural Science Foundation of China under Grant Nos. 61190115, 61173022, 61033015, 60831160525, 61272046, and 60903016, Shandong Provincial Natural Science Foundation under Grant No. ZR2013FQ028, Natural Scientific Research Innovation Foundation in Harbin Institute of Technology under Grant Nos. HIT.NSRIF.2014136 and HIT(WH)201308, and National Science & Technology Pillar Program under Grant Nos. 2012BAA13B01, 2012BAH10F03, 2013BAH17F00.

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

    Xixian Han, Jianzhong Li & Hong Gao

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  1. Xixian Han
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  2. Jianzhong Li
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  3. Hong Gao
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Correspondence to Xixian Han.

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Han, X., Li, J. & Gao, H. TDEP: efficiently processing top-k dominating query on massive data. Knowl Inf Syst 43, 689–718 (2015). https://doi.org/10.1007/s10115-013-0728-5

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