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Use a cluster approach to organize and analyze data inside the cloud

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EN
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EN
In this paper, description of a concept of cloud storage is offered. Cloud data storage is a model of an online storage where the data is being stored in multiple, divided between network servers that are provided for clients’ usage, mostly by a third-party company. The majority of the cloud storages (as opposed to file-exchangers) are offering almost boundless set of functions for free, by only limiting the size of the available storage (mostly a couple of gigabytes). Integrated data mining is being used for extracting potentially useful information from unprocessed data. The methods of data analysis are quite important with cloud computing. The implementation of the methods of integrated data mining inside the cloud will let the users receive the helpful information from non-structured or half-constructed web data sources. The main purpose of this work is to organize huge diverse data coming from different sources into clusters, depending on the type of data.
Twórcy
autor
  • Lviv Polytechnic National University, Lviv, Ukraine
  • Lviv Polytechnic National University, Lviv, Ukraine
  • Lviv Polytechnic National University, Lviv, Ukraine
Bibliografia
  • 1. Boyko N. 2016. Application of mathematical models for improvement of “cloud” data processes organization / N. Boyko // Mathematical Modeling and Computing : scientific journal "Computational problems elektotehniky" – Vol. 3 No. 2 – Founder and Publisher Lviv Polytechnic National University. – P. 111-119.
  • 2. Boyko N. 2017. Use of a cloud storage for implementation informational proce / N. Boyko // ECONTECHMOD : an international quarterly journal on economics of technology and modelling processes – Vol. 2 No. 6. – Branch in Lublin: Polish Academy of Sciences.– P. 3-8.
  • 3. Boyko N. 2016. A look trough methods of intellectual data analysis and their application in informational systems / Nataliya Boyko // Computer Science and Information Technologies CSIT 2016: Materials of the XI International Scientific and Technical Conference CSIT 2016. - Lviv: LvivPolytechnic Publishing House - P. 183-185.
  • 4. Iudin O. K. 2013. Cloud technologies of organization of information corporate networks / O. K. Yudin, R. V. Ziubina, T. V. Ziubin // Informatsiina bezpeka. - T. 11. - No. 3. - Р. 112-127.
  • 5. .Boyko N. 2017. Building computer vision systems using machine learning algorithms / N. Boyko, N. Sokil // ECONTECHMOD : an international quarterly journal on economics of technology and modelling processes – 2017. Vol. 2 No. 6. – Branch in Lublin: Polish Academy of Sciences. – P. 15-20.
  • 6. BSA Global Cloud Computing Scorecard [Electronic resource]. - Access mode: http://cloudscorecard.bsa.org/2013/assets/PDFs/BSA_GlobalCloudScorecard2013.pdf (in Ukrainian)
  • 7. Kroes N. 2011. Towards a European Cloud Computing Strategy / Neelie Kroes [Electronic resource]. - Access mode: http://europa.eu/rapid/pressReleasesAction.do?reference=SPEECH/11/50 (in Ukrainian)
  • 8. Fedorov A. 2012. Golden clouds [Electronic resource] / A. Fedorov // IT-manager. - Access mode: http://www.it-world.ru/download/pdf/itmanager/100/ITM_02-2012_end.pdf (in Russian).
  • 9. Cloud technology. Microsoft for educational institutions2017 [Electronic resource]. -Access mode: http://pro-spo.ru/cloud-technology/3295-external-technologies-microsoft-for educational institutions (in Russian).
  • 10. Seidametova Z. S. 2012. Cloud services in education [Electronic resource] / Z. S. Seidametova, S. N. Seitvelyeva. - Access mode: http://www.smartlearn.ru/site-admin/posts/563-external-services-building.IBM Smart Cloudfor Education. – Access mode: http://www.tadviser.ru/index.php/%D0%9F%D1%80%D0%BE%D0%B4%D1 83%D0%BA%D1%82:IBM_SmartCloud_for_Education(in Russian)
  • 11. Raman T. V. 2008. Cloud computing and equal access for all. Proceedings of the 2008 international cross disciplinary conference on Web accessibility (W4A), Apr. 2008, 1-4. DOI= http://doi.acm.org/10.1145/1368044.1368046.
  • 12. Foster I. 2008. Cloud computing and grid computing 360-degree compared / I. Foster, Y. Zhao, I. Raicu, S. Lu // Grid Computing Environments Workshop, 2008, GCE'08., P. 1-10.
  • 13. Hayes B. 2008. Cloud computing. Communications of the ACM, vol. 51, no. 7. – Р. 9-11.
  • 14. Mladen A. 2008. Cloud computing - Issues, research and implementations. Proceedings of the International Conference on Information Technology Interfaces (ITI’08).- Р. 31-40.
  • 15. Philip K. McKinley, Farshad A. Samimi, Jonathan K. Shapiro, and Chiping Tang. 2006. Service Clouds: A Distributed Infrastructure for Constructing Autonomic Communication Services. Proceedings of the 2nd IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC'06).– Р. 341-348.
  • 16. Robert L. Grossman, Yunhong Gu, Michael Sabala, and Wanzhi Zhang. 2010. Compute and storage clouds using wide area high performance networks. Future Generation Computer Systems, vol. 25, no. 2. Р. 179-183.
  • 17. Milojicic D. 2015. Cloud computing: Interview with Russ Daniels and Franco Travostino. IEEE Internet Computing, vol. 12, no. 5. – Р. 7-9.
  • 18. Nivikov P. S. 1973. Elements of mathematical logic. 2 ed. / P. S. Novikov. - Moscow: Science. - 400 p.
  • 19. Gilbert D. 1947. Fundamentals of Theoretical Logic / Gilbert D., V. Akkeman. - Moscow: GIIL – 302 р.
  • 20. Elkan C. 2003. Using the triangle inequality to accelebrate k-means / C. Elkan // In Proceedings of the Twelfth International Conference on Machine Learning – pp. 147–153.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9594d4fc-d294-4c2a-989c-4f0bf9b0bf55
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