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Big Data Process Advancement

  • Roman JasekEmail author
  • Said Krayem
  • Petr Zacek
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 574)

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.

Keywords

Big data Clustering Parallel clustering Distribution process Distribution file system Formal modelling Event-B Rodin 

Notes

Acknowledgement

This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme project No. LO1303 (MSMT‐7778/2014) and by the European Regional Development Fund under the project CEBIA‐Tech No. CZ.1.05/2.1.00/03.0089. Also supported by grant No. IGA/CebiaTech/2017/007 from IGA (Internal Grant Agency) of Thomas Bata University in Zlin.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of Applied InformaticsTomas Bata University in ZlinZlinCzech Republic

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