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MapReduce Algorithm for Variants of Skyline Queries: Skyband and Dominating Queries

1
Department of Computer Science and Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh
2
Graduate School of Engineering, Hiroshima University, Higashi-Hiroshima 739-8521, Japan
*
Author to whom correspondence should be addressed.
Algorithms 2019, 12(8), 166; https://doi.org/10.3390/a12080166
Received: 28 June 2019 / Revised: 24 July 2019 / Accepted: 7 August 2019 / Published: 13 August 2019
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Abstract

The skyline query and its variant queries are useful functions in the early stages of a knowledge-discovery processes. The skyline query and its variant queries select a set of important objects, which are better than other common objects in the dataset. In order to handle big data, such knowledge-discovery queries must be computed in parallel distributed environments. In this paper, we consider an efficient parallel algorithm for the “K-skyband query” and the “top-k dominating query”, which are popular variants of skyline query. We propose a method for computing both queries simultaneously in a parallel distributed framework called MapReduce, which is a popular framework for processing “big data” problems. Our extensive evaluation results validate the effectiveness and efficiency of the proposed algorithm on both real and synthetic datasets. View Full-Text
Keywords: skyline query; K-skyband query; top-k dominating queries; MapReduce skyline query; K-skyband query; top-k dominating queries; MapReduce
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Siddique, M.A.; Tian, H.; Qaosar, M.; Morimoto, Y. MapReduce Algorithm for Variants of Skyline Queries: Skyband and Dominating Queries. Algorithms 2019, 12, 166.

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