Full Citation

Title: Scalable skyline computation using a balanced pivot selection technique

Citation Type: Journal Article

Publication Year: 2014

ISBN:

ISSN:

DOI: 10.1016/J.IS.2013.05.005

NSFID:

PMCID:

PMID:

Abstract: Skyline queries have recently received considerable attention as an alternative decision-making operator in the database community. The conventional skyline algorithms have primarily focused on optimizing the dominance of points in order to remove non-skyline points as efficiently as possible, but have neglected to take into account the incomparability of points in order to bypass unnecessary comparisons. To design a scalable skyline algorithm, we first analyze a cost model that copes with both dominance and incomparability, and develop a novel technique to select a cost-optimal point, called a pivot point, that minimizes the number of comparisons in point-based space partitioning. We then implement the proposed pivot point selection technique in the existing sorting- and partitioning-based algorithms. For point insertions/deletions, we also discuss how to maintain the current skyline using a skytree, derived from recursive point-based space partitioning. Furthermore, we design an efficient greedy algorithm for the k representative skyline using the skytree. Experimental results demonstrate that the proposed algorithms are significantly faster than the state-of-the-art algorithms.

Url: https://www.sciencedirect.com/science/article/abs/pii/S0306437913000744

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Authors: Lee, Jongwuk; Hwang, Seung-won

Periodical (Full): Information Systems

Issue:

Volume: 39

Pages: 1-21

Data Collections: IPUMS USA

Topics: Methodology and Data Collection

Countries: