The visualization of large graphs accelerated by the parallel nearest neighbors algorithm
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099107" target="_blank" >RIV/61989100:27240/16:86099107 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27740/16:86099107
Result on the web
<a href="http://dx.doi.org/10.1109/BigMM.2016.73" target="_blank" >http://dx.doi.org/10.1109/BigMM.2016.73</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/BigMM.2016.73" target="_blank" >10.1109/BigMM.2016.73</a>
Alternative languages
Result language
angličtina
Original language name
The visualization of large graphs accelerated by the parallel nearest neighbors algorithm
Original language description
The search of k-nearest neighbors (K-NN) is a very common task. The K-NN is utilized in many algorithms and scientific areas like clustering, classification, machine learning, N-body simulation, triangulation, image processing and/or video processing. However, the naive implementation of the K-NN is very slow. There are many novel algorithms for the K-NN search, but they are usually based on the hierarchical clustering. The parallelization of those algorithms is a little bit tricky task. This paper primarily presents a novel parallel method for searching the k-nearest neighbors. An appropriate clustering of a sparse space based on the regular grid and the parallel search of the K-NN using the precomputed clusters are introduced. The whole method is designed for the parallel GPU computation and it is implemented on the CUDA architecture. The presented K-NN is utilized to speed up a force-directed graph layout algorithm, which can visually demonstrate the suitability of found neighbors, because they affect the layout quality. The graphs are widely used in social network analysis, computer networks or large information systems like photographic databases or multimedia databases to visualize relationships between elements. The achieved results and performance tests are presented as well. (C) 2016 IEEE.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
The Second IEEE International Conference on Multimedia Big Data : April 20-22, 2015, Taipei, Taiwan
ISBN
978-1-5090-2178-9
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
9-16
Publisher name
IEEE
Place of publication
New York
Event location
Tchaj-pej
Event date
Apr 20, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000389610000002