Knowledge Discovery in Mega-Spectra Archives

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F14%3APU136101" target="_blank" >RIV/00216305:26230/14:PU136101 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985815:_____/15:00455725 RIV/68407700:21240/15:00309799

Výsledek na webu

<a href="http://www.gothard.hu/gao-mkk/memorabilia/bigdataconf-2014/proceedings/pdf/BigDataConf-proceedings.021-026.pdf" target="_blank" >http://www.gothard.hu/gao-mkk/memorabilia/bigdataconf-2014/proceedings/pdf/BigDataConf-proceedings.021-026.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Knowledge Discovery in Mega-Spectra Archives

Popis výsledku v původním jazyce

The recent progress of astronomical instrumentation resulted in the constructionof multi-object spectrographs with hundreds to thousands of micro-slits or opticalfibers allowing the acquisition of tens of thousands of spectra of celestial objectsper observing night. Currently there are several spectroscopic surveys containingmillions of spectra and much larger are in preparation. Most of the large-scalesurveys are processed spectrum by spectrum in order to estimate physical param-eters of individual objects. The parameters obtained are then used to constructthe better models of space-kinematic structure and evolution of the Universe orits subsystems. Such surveys are, however, very good source of homogenized, pre-processed data for application of machine learning techniques and advanced statis-tical processing common in Astroinformatics. We present challenges of knowledgediscovery process applied to large spectroscopic surveys as well as memory spaceand processing speed demands of current machine learning methods, requiring BigData techniques.

Název v anglickém jazyce

Knowledge Discovery in Mega-Spectra Archives

Popis výsledku anglicky

The recent progress of astronomical instrumentation resulted in the constructionof multi-object spectrographs with hundreds to thousands of micro-slits or opticalfibers allowing the acquisition of tens of thousands of spectra of celestial objectsper observing night. Currently there are several spectroscopic surveys containingmillions of spectra and much larger are in preparation. Most of the large-scalesurveys are processed spectrum by spectrum in order to estimate physical param-eters of individual objects. The parameters obtained are then used to constructthe better models of space-kinematic structure and evolution of the Universe orits subsystems. Such surveys are, however, very good source of homogenized, pre-processed data for application of machine learning techniques and advanced statis-tical processing common in Astroinformatics. We present challenges of knowledgediscovery process applied to large spectroscopic surveys as well as memory spaceand processing speed demands of current machine learning methods, requiring BigData techniques.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: Centrum excelence IT4Innovations</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

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: XXIV

ISBN

978-1-58381-874-9

ISSN

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e-ISSN

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Počet stran výsledku

4

Strana od-do

87-90

Název nakladatele

Astronomical Society of the Pacific

Místo vydání

Calgary

Místo konání akce

Waikoloa, Hawaii

Datum konání akce

29. 9. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000371098000016