The Distributed Cloud Based Engine for Knowledge Discovery in Massive Archives of Astronomical Spectra

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985815%3A_____%2F17%3A00497339" target="_blank" >RIV/67985815:_____/17:00497339 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21240/17:00328879

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

The Distributed Cloud Based Engine for Knowledge Discovery in Massive Archives of Astronomical Spectra

Popis výsledku v původním jazyce

We present VO-CLOUD, the distributed cloud-based engine, providing the user with a comfortable web-based environment for conducting machine learning experiments with different algorithms running on multiple nodes. It allows visual backtracking of the individual input spectra at different stages of preprocessing, which is important for checking the nature of outliers or precision of classification. The engine consists of a single master server, representing the user portal, and several workers, running various types of machine learning tasks. The master holds the database of users and their experiments, predefined configuration parameters for individual machine learning models and a repository for data to be preprocessed. The workers have different capabilities based on the installed libraries and the hardware configuration of their host (e.g. number of CPU cores or GPU card type) and more may be dynamically added to provide new machine learning methods.

Název v anglickém jazyce

The Distributed Cloud Based Engine for Knowledge Discovery in Massive Archives of Astronomical Spectra

Popis výsledku anglicky

We present VO-CLOUD, the distributed cloud-based engine, providing the user with a comfortable web-based environment for conducting machine learning experiments with different algorithms running on multiple nodes. It allows visual backtracking of the individual input spectra at different stages of preprocessing, which is important for checking the nature of outliers or precision of classification. The engine consists of a single master server, representing the user portal, and several workers, running various types of machine learning tasks. The master holds the database of users and their experiments, predefined configuration parameters for individual machine learning models and a repository for data to be preprocessed. The workers have different capabilities based on the installed libraries and the hardware configuration of their host (e.g. number of CPU cores or GPU card type) and more may be dynamically added to provide new machine learning methods.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10308 - Astronomy (including astrophysics,space science)

Projekt

<a href="/cs/project/GA13-08195S" target="_blank" >GA13-08195S: Vysoce škálovatelné paralelní a distribuované metody zpracování vědeckých dat</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Astronomical Data Analysis Software and Systems XXV

ISBN

9781583819081

ISSN

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e-ISSN

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Počet stran výsledku

4

Strana od-do

689-692

Název nakladatele

Astronomical Society of the Pacific

Místo vydání

San Francisco

Místo konání akce

Sydney

Datum konání akce

25. 10. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000442046200152