An Efficient Angular Space Partitioning Based Skyline Query Processing Using Sampling-Based Pruning


KIPS Transactions on Software and Data Engineering, Vol. 6, No. 1, pp. 1-8, Jan. 2017
10.3745/KTSDE.2017.6.1.1,   PDF Download:
Keywords: Skyline Computation, MapReduce, Pruning, Data Sampling
Abstract

Given a multi-dimensional dataset of tuples, a skyline query returns a subset of tuples which are not ‘dominated’ by any other tuples. Skyline query is very useful in Big data analysis since it filters out uninteresting items. Much interest was devoted to the MapReduce-based parallel processing of skyline queries in large-scale distributed environment. There are three requirements to improve parallelism in MapReduced-based algorithms: (1) workload should be well balanced (2) avoid redundant computations (3) Optimize network communication cost. In this paper, we introduce MR-SEAP(MapReduce sample Skyline object Equality Angular Partitioning), an efficient angular space partitioning based skyline query processing using sampling-based pruning, which satisfies requirements above. We conduct an extensive experiment to evaluate MR-SEAP.


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Cite this article
[IEEE Style]
W. Choi, M. Kim, G. Diana, J. Chung, S. Jung, "An Efficient Angular Space Partitioning Based Skyline Query Processing Using Sampling-Based Pruning," KIPS Transactions on Software and Data Engineering, vol. 6, no. 1, pp. 1-8, 2017. DOI: 10.3745/KTSDE.2017.6.1.1.

[ACM Style]
Woosung Choi, Minseok Kim, Gromyko Diana, Jaehwa Chung, and Soonyong Jung. 2017. An Efficient Angular Space Partitioning Based Skyline Query Processing Using Sampling-Based Pruning. KIPS Transactions on Software and Data Engineering, 6, 1, (2017), 1-8. DOI: 10.3745/KTSDE.2017.6.1.1.