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Proceedings Paper

Application of a fast skyline computation algorithm for serendipitous searching problems
Author(s): Kenichi Koizumi; Kei Hiraki; Mary Inaba
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Paper Abstract

Skyline computation is a method of extracting interesting entries from a large population with multiple attributes. These entries, called skyline or Pareto optimal entries, are known to have extreme characteristics that cannot be found by outlier detection methods. Skyline computation is an important task for characterizing large amounts of data and selecting interesting entries with extreme features. When the population changes dynamically, the task of calculating a sequence of skyline sets is called continuous skyline computation. This task is known to be difficult to perform for the following reasons: (1) information of non-skyline entries must be stored since they may join the skyline in the future; (2) the appearance or disappearance of even a single entry can change the skyline drastically; (3) it is difficult to adopt a geometric acceleration algorithm for skyline computation tasks with high-dimensional datasets. Our new algorithm called jointed rooted-tree (JR-tree) manages entries using a rooted tree structure. JR-tree delays extend the tree to deep levels to accelerate tree construction and traversal. In this study, we presented the difficulties in extracting entries tagged with a rare label in high-dimensional space and the potential of fast skyline computation in low-latency cell identification technology.

Paper Details

Date Published: 20 February 2018
PDF: 5 pages
Proc. SPIE 10505, High-Speed Biomedical Imaging and Spectroscopy III: Toward Big Data Instrumentation and Management, 1050514 (20 February 2018); doi: 10.1117/12.2288156
Show Author Affiliations
Kenichi Koizumi, The Univ. of Tokyo (Japan)
Kei Hiraki, The Univ. of Tokyo (Japan)
Mary Inaba, The Univ. of Tokyo (Japan)

Published in SPIE Proceedings Vol. 10505:
High-Speed Biomedical Imaging and Spectroscopy III: Toward Big Data Instrumentation and Management
Kevin K. Tsia; Keisuke Goda, Editor(s)

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