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dc.title Big data process advancement en
dc.contributor.author Jašek, Roman
dc.contributor.author Krayem, Said
dc.contributor.author Žáček, Petr
dc.relation.ispartof Advances in Intelligent Systems and Computing
dc.identifier.issn 2194-5357 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 978-3-319-57263-5
dc.date.issued 2017
utb.relation.volume 574
dc.citation.spage 379
dc.citation.epage 396
dc.event.title 6th Computer Science On-line Conference, CSOC 2017
dc.event.sdate 2017-04-26
dc.event.edate 2017-04-29
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer Verlag
dc.identifier.doi 10.1007/978-3-319-57264-2_39
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-319-57264-2_39
dc.subject Big data en
dc.subject Clustering en
dc.subject Distribution file system en
dc.subject Distribution process en
dc.subject Event-B en
dc.subject Formal modelling en
dc.subject Parallel clustering en
dc.subject Rodin en
dc.description.abstract Information in this era is thriving to be maintained on a verity of sources. Data is available in different patterns and forms. Combining and processing all different types of datasets in a heterogeneity database is near to impossible, specifically, if the information is moving and changing on many different sources on a continuous basis. Information is represented in different modules and nowadays processing data from various sources can lead to critical risk assessment results. Big Data is a concept introduced to cover the use of different techniques serving the desired goals by processing the given information. Processing huge amount of data is a big challenge for a single machine to perform, in this paper we will discuss this idea and demonstrate a module of clustered machines to work as a single entity towards achieving the desired tasks while working on parallel cohesively. The idea of a solution to combine different machines of different specification processing and power in a single cluster and then distributing input data of various data fairly to most powerful processing and well-designed data type machine in the cluster. Distribution of input data and storing mechanism will depend on machine specification, data processing, the power of a machine, balance loading and data type. We present our suggestion solving method by using Event-B based approach, the Key features of Event-B are the use of set theory as a modelling notation and we propose using the Rodin modelling tool for Event-B that integrates modelling and proving. © Springer International Publishing AG 2017. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1007383
utb.identifier.obdid 43877333
utb.identifier.scopus 2-s2.0-85018673810
utb.identifier.wok 000405339200039
utb.source d-scopus
dc.date.accessioned 2017-09-08T12:14:49Z
dc.date.available 2017-09-08T12:14:49Z
dc.description.sponsorship Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme [LO1303 (MSMT-7778/2014)]; European Regional Development Fund under the project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; IGA (Internal Grant Agency) of Thomas Bata University in Zlin [IGA/CebiaTech/2017/007]
utb.contributor.internalauthor Jašek, Roman
utb.contributor.internalauthor Krayem, Said
utb.contributor.internalauthor Žáček, Petr
utb.fulltext.affiliation Roman Jasek, Said Krayem, Petr Zacek Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic {jasek,zacek}@fai.utb.cz, drsaid@seznam.cz
utb.fulltext.dates -
utb.fulltext.faculty Faculty of Applied Informatics
utb.fulltext.faculty Faculty of Applied Informatics
utb.fulltext.faculty Faculty of Applied Informatics
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