Generalized formal model of big data

• Autorzy: Shakhovska N. , Veres O. , Hirnyak M.
• Języki publikacji: EN

Abstrakty

EN

This article dwells on the basic characteristic features of the Big Data technologies. It is analyzed the existing definition of the "big data" term. The article proposes and describes the elements of the generalized formal model of big data. It is analyzed the peculiarities of the application of the proposed model components. It is described the fundamental differences between Big Data technology and business analytics. Big Data is supported by the distributed file system Google File System technology, Cassandra, HBase, Lustre and ZFS, by the MapReduce and Hadoop programming constructs and many other solutions. According to the experts, such as McKinsey Institute, the manufacturing, healthcare, trade, administration and control of individual movements undergo the transformations under the influence of the Big Data.

Słowa kluczowe

EN

analysis; data; model; big data; information technology

• Wydawca: Polish Academy of Sciences, Branch in Lublin
• Czasopismo: ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
• Rocznik: 2016
• Tom: Vol. 5, No 2
• Strony: 33--38
• Opis fizyczny: Bibliogr. 20 poz., wz.

Twórcy

autor

Shakhovska N.

• University of Economy, Bydgoszcz

autor

Veres O.

• Lviv Polytechnic National University

autor

Hirnyak M.

• Lviv Polytechnic National University

Bibliografia

• 1. “Big Data”. 2016. http://www.tadviser.ru/index.php/Статья:Большие_данные_(Big_Data)
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• 4. Chris Preimesberger. 2011. Hadoop, Yahoo, 'Big Data' Brighten BI Future. eWeek (15 August 2011). http://www.eweek.com/c/a/Data-Storage/TBAHadoop-Yahoo-Big-Data-Brightens-BI-Future-254079.
• 5. Gartner. 2011. Gartner Says Solving 'Big Data' Challenge Involves More Than Just Managing Volumes of Data. Gartner (27 June 2011). http://www.gartner.com/newsroom/id/1731916.
• 6. Shakhovska N.B., Bolubash Yu.Ja., Veres O.M. 2015. Big Data Federated Repository Model // The Experience of Designing and Application of CAD Systems in Microelectronics (СADMS’2015) Proc. of the XIII-th Int. Conf., (Polyana-Svalyava (Zakarpattya), Ukraine, 24-27 February, 2015). – Lviv: Publishing Lviv Polytechnic, 2015.– Pp. 382- 384. (in Ukrainian).
• 7. Shakhovska N.B., Bolubash Yu.Ja., Veres O.M. 2014. Big data organizing in a distributed environment. Computer Science and Automation. Donetsk. Ukraine. n. 2(27), Pp. 147-155. (in Ukrainian).
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• 10. Shakhovska N.B. 2011. Formal presentation of data space as an algebraic system. System Research and Information Technologies / National Academy of Sciences of Ukraine, Institute for Applied Systems Analysis. – Kyiv, 2011. – № 2. – Pp. 128 – 140. (in Ukrainian).
• 11. Shakhovska N., Bolubash Yu. 2013. Working with Big Data as indicators of socio-ecological-economic development. Eastern-Euro-pean Journal of Enterprise Technologies, T. 5, V. 2(65), 2013., Pp.4-8. (in Ukrainian).
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• 16. Kossmann D., Dittrich J.P. 2006 Personal Data Spaces. Available online at: http://www.inf.ethz.ch/news/focus/res_focus/feb_2006/index_DE.
• 17. Stonebraker M., Abadi D., DeWitt D. J., Madden S., Pavlo A., Rasin A. 2012 MapReduce and Parallel DBMSs: Friends or Foes. Communications of the ACM (53:1). Pp. 64-71.
• 18. Laney D. 2001 3D Data Management: Controlling Data Volume, Velocity and Variety. Gartner.
• 19. Pedrycz W., Chen S.-M. 2015. Information Granularity, Big Data, and Computational Intelligence, Studies in Big Data 8, DOI: 10.1007/978-3-319-08254-7, Springer International Publishing Switzerland.
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Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę