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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F14%3A00224799%21RIV15-MSM-21230___
rdf:type
n4:Vysledek skos:Concept
dcterms:description
The trend of large scale data production is observed not only within web companies, but is entering also other domains including automation domain. Smart sensors and smart devices contribute to growing amounts of data that need to be processed. An example of processing is prediction for better control, clustering for more effective maintenance, or improving the overall production in general. The so called Big Data paradigm shows new ways of handling bigger amounts of various data, including providing technologies that are able to handle them in an effective way. This paper examines the utilization of Big Data technologies for industry automation domain. The approach is illustrated on time series data measured from a passive house with the goal of detecting specific events. We show how the Big Data technologies allow data analysis that would be hard with traditional approaches. The trend of large scale data production is observed not only within web companies, but is entering also other domains including automation domain. Smart sensors and smart devices contribute to growing amounts of data that need to be processed. An example of processing is prediction for better control, clustering for more effective maintenance, or improving the overall production in general. The so called Big Data paradigm shows new ways of handling bigger amounts of various data, including providing technologies that are able to handle them in an effective way. This paper examines the utilization of Big Data technologies for industry automation domain. The approach is illustrated on time series data measured from a passive house with the goal of detecting specific events. We show how the Big Data technologies allow data analysis that would be hard with traditional approaches.
dcterms:title
Semantic Heterogeneity Reduction for Big Data in Industrial Automation Semantic Heterogeneity Reduction for Big Data in Industrial Automation
skos:prefLabel
Semantic Heterogeneity Reduction for Big Data in Industrial Automation Semantic Heterogeneity Reduction for Big Data in Industrial Automation
skos:notation
RIV/68407700:21230/14:00224799!RIV15-MSM-21230___
n3:aktivita
n18:S
n3:aktivity
S
n3:dodaniDat
n10:2015
n3:domaciTvurceVysledku
n11:2639637 n11:8052565
n3:druhVysledku
n7:D
n3:duvernostUdaju
n14:S
n3:entitaPredkladatele
n9:predkladatel
n3:idSjednocenehoVysledku
44375
n3:idVysledku
RIV/68407700:21230/14:00224799
n3:jazykVysledku
n20:eng
n3:klicovaSlova
Automation; Big Data; Companies; Production; Real-time Systems; Temperature Measurement; Time Series Analysis
n3:klicoveSlovo
n6:Time%20Series%20Analysis n6:Big%20Data n6:Real-time%20Systems n6:Automation n6:Temperature%20Measurement n6:Production n6:Companies
n3:kontrolniKodProRIV
[BA19BF633EE9]
n3:mistoKonaniAkce
Jasná pod Chopkom
n3:mistoVydani
Praha
n3:nazevZdroje
Proceedings of the 13th Annual Conference Znalosti 2014
n3:obor
n13:JC
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:rokUplatneniVysledku
n10:2014
n3:tvurceVysledku
Jirkovský, Václav Obitko, Marek
n3:typAkce
n5:WRD
n3:zahajeniAkce
2014-09-26+02:00
s:numberOfPages
10
n19:hasPublisher
Vysoká škola ekonomická v Praze
n15:isbn
978-80-245-2054-4
n16:organizacniJednotka
21230