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Tytuł artykułu

Dataspace architecture and manage its components class projection

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Języki publikacji
EN
Abstrakty
EN
Big Data technology is described. Big data is a popular term used to describe the exponential growth and availability of data, both structured and unstructured. There is constructed dataspace architecture. Dataspace has focused solely – and passionately – on providing unparalleled expertise in business intelligence and data warehousing strategy and implementation. Dataspaces are an abstraction in data management that aims to overcome some of the problems encountered in data integration system. In our case it is block vector for heterogeneous data representation. Traditionally, data integration and data exchange systems have aimed to offer many of the purported services of dataspace systems. Dataspaces can be viewed as a next step in the evolution of data integration architectures, but are distinct from current data integration systems in the following way. Data integration systems require semantic integration before any services can be provided. Hence, although there is not a single schema to which all the data conforms and the data resides in a multitude of host systems, the data integration system knows the precise relationships between the terms used in each schema. As a result, significant up-front effort is required in order to set up a data integration system. For realization of data integration from different sources we used SQL Server Integration Services, SSIS. For developing the portal as an architectural pattern there is used pattern Model-View-Controller (MVC). There is specifics debug operation data space as a complex system. The query translator in Backus/Naur Form is give.
Twórcy
  • Information Systems and Networks Department, Lviv Polytechnic National University S.Bandera str, 28, Lviv, Ukraine
autor
  • Information Systems and Networks Department, Lviv Polytechnic National University S.Bandera str, 28, Lviv, Ukraine
Bibliografia
  • 1. Shakhovska N. 2010. “Programming and algorithmic ware of datawarhouses and dataspaces”. Ministry of Education and Science of Ukraine, National University "Lviv Polytechnic" .- Lviv: Publishing House of Lviv Polytechnic. 194 (in Ukrainian).
  • 2. Shakhovska N. 2011. “Methods of processing data using consolidated data space”. Problems of Development, National Academy of Sciences of Ukraine, Institute of Software of NAS of Ukraine, № 4. 72-84 (in Ukrainian).
  • 3. Matov O. 2009. “Modern technology integration of information resources”. Temple Storage and Processing data. T. 11, № 1. 33-42 (in Ukrainian).
  • 4. The Open Archives Initiative Protocol for Metadata Harvesting Protocol Version 2.0 of 2002. http://www.openarchives.org /OAI/2.0/openarchivesprotocol.htm.
  • 5. Gritsenko V. 2001. “Information Technology: Trends in the development”. Control systems and machines. - № 5. - S.3-20 (in Russian).
  • 6. White, T. 2012. Hadoop: The Definitive Guide. O'Reilly Media. p. 3. ISBN 978-1-4493-3877-0.
  • 7. IBM What is big data? - Bringing big data to the enterprise. www.ibm.com. Retrieved 2013.
  • 8. Oracle and FSN, "Mastering Big Data: CFO Strategies to Transform Insight into Opportunity", 2012.
  • 9. Jacobs, A. 2009. "The Pathologies of Big Data". ACMQueue. http://queue.acm.org/detail.cfm?id=1563874
  • 10. Magoulas, Roger and Lorica, B. 2009. "Introduction to Big Data". Release 2.0 (Sebastopol CA: O’Reilly Media. 11.
  • 11. Snijders, C., Matzat, U., and Reips, U.-D. 2012. ‘Big Data’: Big gaps of knowledge in the field of Internet. International Journal of Internet Science, 7, 1-5. http://www.ijis.net/ijis7_1/ijis7_1_editorial.html.
  • 12. Laney, D. 2001. "3D Data Management: Controlling Data Volume, Velocity and Variety". Gartner. Retrieved. – 23-28.
  • 13. Beyer, M. 2011. "Gartner Says Solving 'Big Data' Challenge Involves More Than Just Managing Volumes of Data". Gartner. Archived from the original on 10 July 2011. Retrieved 13 July 2011.
  • 14. Laney, D. 2012. "The Importance of 'Big Data': A Definition". Gartner. Retrieved 21 June 2012.
  • 15. National Research Council. 2008. Behavioral Modelling and Simulation: From Individuals to Societies , Committee on Organizational Modeling: From Individuals to Societies, G. L. Zacharias, J. MacMillan and S. B. Van Heme l (eds.), Board on Behavioral, Cognitive, and Sensory Sciences, Division of Behavioral and Social Sciences and Education, Washington, DC: The National Academies Press.
  • 16. 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.
  • 17. Connolly, T. M. and Begg, E. C. 2010. Database Systems: A practical Approach to Design, Implementation, and Management. 5th ed. London: Addison-Wesley.
  • 18. Dreyfus P. 2012. Joint simulation of stand dynamics and landscape evolution using a tree-level model for mixed uneven -aged forests. Annals of Forest Science # 69. 283- 303.
  • 19. Backus, J.W. 1959. "The syntax and semantics of the proposed international algebraic language of the Zurich ACM-GAMM Conference". Proceedings of the International Conference on Information Processing. UNESCO.. 125-132.
  • 20. Farrell, James A. 1995. "Compiler Basics". Extended Backus Naur Form. Archived from the original on 5 June 2011.
  • 21. Kalyuzhna N. and Golovkova К. 2013. “Structural contradictions in control system by enterprise as function of associate administrative decisions”. Econtechmod. An international quarterly journal. Poland, Lublin – Rzeszow. Vol. 02. No. 3. 33-40.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6d842d89-927b-41bc-aeb8-78855565ef2d
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