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Statements

Subject Item
n2:RIV%2F61989100%3A27740%2F14%3A86092546%21RIV15-MSM-27740___
rdf:type
n12:Vysledek skos:Concept
dcterms:description
Spectra analysis on large datasets is in focus of this paper. First of all we discuss a method useful for spectra analysis – analytical programming and its implementation. Our goal is to create mathematical formulas of emission lines from spectra, which are characteristic for Be stars. One issue in performing this task is symbolic regression, which represents the process in our application, when measured data fits the best represented mathematical formula. In past this was only a human domain; nowadays, there are computer methods, which allow us to do it more or less effectively. A novel method in symbolic regression, compared to genetic programming and grammar evolution, is analytic programming. The aim of this work is to verify the efficiency of the parallel approach of this algorithm, using CUDA architecture. Next we will discuss parallel implementation of random decision forest (RDF) to classify huge amounts of various spectra. The mathematical formulas obtained via AP will be used to reduce attributes of explored spectra. Our goal is to propose scalable algorithm for classification of such data, which will preferably need only one pass over data, while maintaining acceptable accuracy. Later we will try to create module compatible with VO and DAta Mining and Exploration project. Spectra analysis on large datasets is in focus of this paper. First of all we discuss a method useful for spectra analysis – analytical programming and its implementation. Our goal is to create mathematical formulas of emission lines from spectra, which are characteristic for Be stars. One issue in performing this task is symbolic regression, which represents the process in our application, when measured data fits the best represented mathematical formula. In past this was only a human domain; nowadays, there are computer methods, which allow us to do it more or less effectively. A novel method in symbolic regression, compared to genetic programming and grammar evolution, is analytic programming. The aim of this work is to verify the efficiency of the parallel approach of this algorithm, using CUDA architecture. Next we will discuss parallel implementation of random decision forest (RDF) to classify huge amounts of various spectra. The mathematical formulas obtained via AP will be used to reduce attributes of explored spectra. Our goal is to propose scalable algorithm for classification of such data, which will preferably need only one pass over data, while maintaining acceptable accuracy. Later we will try to create module compatible with VO and DAta Mining and Exploration project.
dcterms:title
Big data spectra analysis using analytical programming and random decision forests Big data spectra analysis using analytical programming and random decision forests
skos:prefLabel
Big data spectra analysis using analytical programming and random decision forests Big data spectra analysis using analytical programming and random decision forests
skos:notation
RIV/61989100:27740/14:86092546!RIV15-MSM-27740___
n3:aktivita
n7:S n7:P
n3:aktivity
P(EE.2.3.20.0072), P(GA13-08195S), S
n3:dodaniDat
n16:2015
n3:domaciTvurceVysledku
n10:3433390
n3:druhVysledku
n22:D
n3:duvernostUdaju
n14:S
n3:entitaPredkladatele
n18:predkladatel
n3:idSjednocenehoVysledku
5251
n3:idVysledku
RIV/61989100:27740/14:86092546
n3:jazykVysledku
n20:eng
n3:klicovaSlova
Virtual observatory; Symbolic regression; Spectra analysis; Random decision forest; Parallel implementation; Evolutionary algorithm; Differential evolution; Data mining; CUDA; Analytical programming
n3:klicoveSlovo
n5:CUDA n5:Evolutionary%20algorithm n5:Differential%20evolution n5:Analytical%20programming n5:Parallel%20implementation n5:Symbolic%20regression n5:Random%20decision%20forest n5:Virtual%20observatory n5:Spectra%20analysis n5:Data%20mining
n3:kontrolniKodProRIV
[45216A5722E6]
n3:mistoKonaniAkce
Ho Chi Minh City
n3:mistoVydani
Heidelberg
n3:nazevZdroje
Lecture Notes in Computer Science. Volume 8838
n3:obor
n6:IN
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
4
n3:projekt
n13:GA13-08195S n13:EE.2.3.20.0072
n3:rokUplatneniVysledku
n16:2014
n3:tvurceVysledku
Drábik, Peter Šaloun, Petr Zelinka, Ivan Bucko, Jaroslav
n3:typAkce
n21:WRD
n3:zahajeniAkce
2014-11-05+01:00
s:issn
0302-9743
s:numberOfPages
12
n4:doi
10.1007/978-3-662-45237-0_26
n11:hasPublisher
Springer-Verlag
n9:isbn
978-3-662-45236-3
n19:organizacniJednotka
27740