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Efficient Approaches to k Representative G-Skyline Queries

Efficient Approaches to k Representative G-Skyline Queries The G-Skyline (GSky) query is a powerful tool to analyze optimal groups in decision support. Compared with other group skyline queries, it releases users from providing an aggregate function. Besides, it can get much comprehensive results without overlooking some important results containing non-skylines. However, it is hard for the users to make sensible choices when facing so many results the GSky query returns, especially over a large, high-dimensional dataset or with a large group size. In this article, we investigate k representative G-Skyline (kGSky) queries to obtain a manageable size of optimal groups. The kGSky query can also inherit the advantage of the GSky query; its results are representative and diversified. Next, we propose three exact algorithms with novel techniques including an upper bound pruning, a grouping strategy, a layered optimum strategy, and a hybrid strategy to efficiently process the kGSky query. Consider these exact algorithms have high time complexity and the precise results are not necessary in many applications. We further develop two approximate algorithms to trade off some accuracy for efficiency. Extensive experiments on both real and synthetic datasets demonstrate the efficiency, scalability, and accuracy of the proposed algorithms. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Knowledge Discovery from Data (TKDD) Association for Computing Machinery

Efficient Approaches to k Representative G-Skyline Queries

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2020 ACM
ISSN
1556-4681
eISSN
1556-472X
DOI
10.1145/3397503
Publisher site
See Article on Publisher Site

Abstract

The G-Skyline (GSky) query is a powerful tool to analyze optimal groups in decision support. Compared with other group skyline queries, it releases users from providing an aggregate function. Besides, it can get much comprehensive results without overlooking some important results containing non-skylines. However, it is hard for the users to make sensible choices when facing so many results the GSky query returns, especially over a large, high-dimensional dataset or with a large group size. In this article, we investigate k representative G-Skyline (kGSky) queries to obtain a manageable size of optimal groups. The kGSky query can also inherit the advantage of the GSky query; its results are representative and diversified. Next, we propose three exact algorithms with novel techniques including an upper bound pruning, a grouping strategy, a layered optimum strategy, and a hybrid strategy to efficiently process the kGSky query. Consider these exact algorithms have high time complexity and the precise results are not necessary in many applications. We further develop two approximate algorithms to trade off some accuracy for efficiency. Extensive experiments on both real and synthetic datasets demonstrate the efficiency, scalability, and accuracy of the proposed algorithms.

Journal

ACM Transactions on Knowledge Discovery from Data (TKDD) – Association for Computing Machinery

Published: Jul 6, 2020

Keywords: Approximate algorithms

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