NDLI: Scalable skyline computation using object-based space partitioning

Content Provider	ACM Digital Library
Author	Mamoulis, Nikos Cheung, David W. Zhang, Shiming
Abstract	The skyline operator returns from a set of multi-dimensional objects a subset of superior objects that are not dominated by others. This operation is considered very important in multi-objective analysis of large datasets. Although a large number of skyline methods have been proposed, the majority of them focuses on minimizing the I/O cost. However, in high dimensional spaces, the problem can easily become CPU-bound due to the large number of computations required for comparing objects with current skyline points while scanning the database. Based on this observation, we propose a dynamic indexing technique for skyline points that can be integrated into state-of-the-art sort-based skyline algorithms to boost their computational performance. The new indexing and dominance checking approach is supported by a theoretical analysis, while our experiments show that it scales well with the input size and dimensionality not only because unnecessary dominance checks are avoided but also because it allows efficient dominance checking with the help of bitwise operations.
Starting Page	483
Ending Page	494
Page Count	12
File Format	PDF
ISBN	9781605585512
DOI	10.1145/1559845.1559897
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-06-29
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Skyline Preference Space partitioning
Content Type	Text
Resource Type	Article

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Scalable skyline computation using object-based space partitioning