| Autor: |
Mary Inaba, Kei Hiraki, Kenichi Koizumi |
| Rok vydání: |
2018 |
| Předmět: |
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| Zdroj: |
High-Speed Biomedical Imaging and Spectroscopy III: Toward Big Data Instrumentation and Management. |
| DOI: |
10.1117/12.2288156 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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