Distributed processing of elevation data by means of apache hadoop in a small cluster
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F13%3A39896621" target="_blank" >RIV/00216275:25410/13:39896621 - isvavai.cz</a>
Výsledek na webu
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DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Distributed processing of elevation data by means of apache hadoop in a small cluster
Popis výsledku v původním jazyce
Geoinformation technologies require fast processing of high and quickly increasing volumes of all types of spatial data. Parallel computational approach and distributed systems represent technologies which are able to provide required services, with reasonable costs. MapReduce is one example of such approach. It has been successfully implemented in large clusters in several instances. The applications include spatial and imagery data processing. The contribution deals with its implementation and operational performance using only a very small cluster (consisting of a few commodity personal computers) to process large-volume spatial data. Open-source implementation of MapReduce, named, Apache Hadoop, is used. The contribution is focused on a low-price solution and it deals with speed of processing and distribution of processed files. Authors run several experiments to evaluate the benefit of distributed data processing in a small-sized cluster and to find possible limitations. Size of p
Název v anglickém jazyce
Distributed processing of elevation data by means of apache hadoop in a small cluster
Popis výsledku anglicky
Geoinformation technologies require fast processing of high and quickly increasing volumes of all types of spatial data. Parallel computational approach and distributed systems represent technologies which are able to provide required services, with reasonable costs. MapReduce is one example of such approach. It has been successfully implemented in large clusters in several instances. The applications include spatial and imagery data processing. The contribution deals with its implementation and operational performance using only a very small cluster (consisting of a few commodity personal computers) to process large-volume spatial data. Open-source implementation of MapReduce, named, Apache Hadoop, is used. The contribution is focused on a low-price solution and it deals with speed of processing and distribution of processed files. Authors run several experiments to evaluate the benefit of distributed data processing in a small-sized cluster and to find possible limitations. Size of p
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
IN - Informatika
OECD FORD obor
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Návaznosti výsledku
Projekt
Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2013
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
ICSOFT 2013 - Proceedings of the 8th International Joint Conference on Software Technologies
ISBN
978-989-8565-68-6
ISSN
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e-ISSN
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Počet stran výsledku
5
Strana od-do
340-344
Název nakladatele
Institute for Systems and Technologies of Information, Control and Communication (INSTICC)
Místo vydání
Setubal
Místo konání akce
Reykjavík
Datum konání akce
29. 7. 2013
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
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