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Big Data Model "Entity and Features"

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Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The article deals with the problem which led to Big Data. Big Data information technology is the set of methods and means of processing different types of structured and unstructured dynamic large amounts of data for their analysis and use of decision support. Features of NoSQL databases and categories are described. The developed Big Data Model “Entity and Features” allows determining the distance between the sources of data on the availability of information about a particular entity. The information structure of Big Data has been devised. It became a basis for further research and for concentrating on a problem of development of diverse data without their preliminary integration.
Słowa kluczowe
Twórcy
  • Lviv Polytechnic National University
autor
  • Lviv Polytechnic National University
autor
  • Lviv Polytechnic National University
Bibliografia
  • 1. Pedrycz W., and S.-M. Chen. 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.
  • 2. Srinivasa, S., and V. Bhatnagar. 2012. Big data analytics. In: Proceedings of the First International Conference on Big Data Analytics BDA’2012. Lecture Notes in Computer Science, vol. 7678. Springer, New Delhi, 24–26 Dec 2012.
  • 3. Butakova М.А., Klimanskaya Е.V., and Yants V.I. 2013. Measure of informative similarity for the analysis of semi-structured information. Modern problems of science and education. n. 6; Available online at: <http://www.science-education.ru/113- 11307>.
  • 4. Chang F., Dean J., Ghemawat, S., Hsieh W., Wallach D., Burrows M., Chandra T., Fikes A., Gruber R. 2006. Bigtable: A Distributed Storage System for Structured Data, Research (PDF), Google.
  • 5. Papakonstantinov F. and Widom J. 2005. Object exchange across heterogeneous information sources.11-th International Conference on Data Engineering (ICDE’05). 251-261.
  • 6. Feng Z., Hsu W. and Li M. 2005. Efficient pattern discovery for semi-structured data. 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI-05). 301-309.
  • 7. Shakhovska N. 2010. Software and algorithmic software of data storage and data spaces: monograph; Acting Lviv Polytechnic Nat. University. Lviv: Izd Lviv Polytechnic, 194. (in Ukrainian).
  • 8. Shakhovska N. 2008. Features Data spaces modeling. Proceedings of Lviv Polytechnic National University. Lviv: Izd Lviv Polytechnic Nat. Univ, Computer Engineering and Information Technology. 608:145 – 154. (in Ukrainian).
  • 9. Shakhovska N., Medykovsky M., and Stakhiv P. 2013. Application of algorithms of classification for uncertainty reduction/ Przeglad Elektrotechniczny. 89(4):284-286
  • 10. Кut V. 2014. Consolidated model of remote and consulting center of people with special needs. Proceedings of Lviv Polytechnic National University. Information systems and networks. 783:120–127. (in Ukrainian).
  • 11. Kushniretska I., Kushniretska O., and Berko A. 2014. Analysis of information resources integration system of dynamic semistructured data in a Webenvironment. Proceedings of Lviv Polytechnic National University. Information systems and networks. 805:162-169. (in Ukrainian).
  • 12. The daily volume of transactions in Bitcoin overcame the barrier of 100 000: Available online at: <http://vkurse.ua/ua/business/ezhednevnyy-obemtranzakciy- v-bitcoin.html>.
  • 13. Data Growth, Business Opportunities, and the IT Imperatives Available online at: <http://www.emc. com/leadership/digital-niverse/2014iview/executivesummary. htm>.
  • 14. Shakhovska N., Bolubash Yu, and Veres O. 2015. Big Data Federated Repository Model. Proc. Of СADMS’2015. Lviv: Lviv Polytechnic Publishing. 382-384. (in Ukrainian).
  • 15. Shakhovska N., Bolubash Y, and Veres O. 2014. Big data organizing in a distributed environment. Computer Science and Automation. Donetsk. Ukraine. 2(27):147-155. (in Ukrainian).
  • 16. Shakhovska N. 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. 2:128 – 140. (in Ukrainian).
  • 17. Shakhovska N. and Bolubash Y. 2013. Working with Big Data as indicators of socio-ecologicaleconomic development. Eastern-European Journal of Enterprise Technologies, 2(65):5. (in Ukrainian).
  • 18. Kalyuzhna N. and Golovkova К. 2013. Structural contradictions in control system by enterprise asfunction of associate administrative decisions. EconTechMod: an International Quarterly Journal on Economics in Technology, new Technologies and Modelling Processes. Krakiv-Lviv, 2(3):33-40.
  • 19. Magoulas R., and Ben L. 2009 Big data: Technologies and techniques for large scale data, Release 2.0
  • 20. Kossmann D., Dittrich J.P. 2006 Personal Data Spaces. Available online at: http://www.inf.ethz.ch/news/focus/res_fo cus/ feb_2006/index_DE.
  • 21. Stonebraker M., Abadi D., DeWitt D. J., Madden S., Pavlo A., and Rasin A. 2012 MapReduce and Parallel DBMSs: Friends or Foes. Communications of the ACM (53:1). 64-71.
  • 22. Laney D. 2001 3D Data Management: Controlling Data Volume, Velocity and Variety. Gartner.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-73faf6ad-8e3f-456e-8f1e-9294b353e4f8
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