350 rub
Journal Highly available systems №4 for 2016 г.
Article in number:
Teaching the methods of development of analytical information systems based on cloud computing (Microsoft Azure as an example)
Authors:
V.I. Munerman - Ph. D. (Eng.), Associate Professor, Department of Informatics, Smolensk State University E-mail: vimoon@gmail.com T.A. Samoylova - Ph. D. (Eng.), Associate Professor, Department of Informatics, Smolensk State University E-mail: tatsam@hotbox.ru
Abstract:
The article proposes a course that focuses on the training of specialists in the field of software development of analytical information systems (AIS). Programmer-developer must own the technology for receiving data from different input streams, to be able to organize their storage and maintenance of up to date data binding with analytic algorithms and display the results of the analysis using a variety of user interfaces. At the same time one of the most important is the ability to organize the networking into all subsystems. Based on these assumptions, the proposed course, which focused on the training of the programmer-developer, is built. The basis of the course lays the use of cloud technologies. Analysis of well-known clouds is made. Microsoft Azure technology is chosen based on the analysis results. As the basic architecture of the AIS the service-oriented architecture is chosen. Three types of ar-chitectures, those based on Web-services are considered: Web-service, that focuses on analytical tools of database management sys-tems; Web-service, that focuses on analytical tools Machine Learning; Web-service, that based on the Python programming technolo-gies. The course consists of six main parts: 1. The development of data analysis procedures with using the Azure SQL Server means; 2. Creating a cloud service for the IO and the computing management; 3. Creating a Web-service using the Azure Machine Learning Studio; 4. Creating a Web-service using Python tools for Visual Studio.NET; 5. The development of means of access to the IO Web-service from the user application; 6. The development of means of access to the cloud analytical Web-service from the user application.
Pages: 3-11
References

 

  1. http://www.klerk.ru/boss/articles/442444/.
  2. Sinicyn I.N., SHalamov A.S. Lekcii po teorii sistem integrirovannojj logisticheskojj podderzhki. M.: TORUS PRESS. 2012. 624 s.
  3. Stupnikov S.A., Skvorcov N.A., Budzko V.I., Zakharov V.N., Kalinichenko L.A. Metody unifikacii netradicionnykh modelejj dannykh // Sistemy vysokojj dostupnosti. 2014. T. 10. № 1. S. 18−40.
  4. Filippov S.A., Zakharov V.N., Stupnikov S.A., Kovalev D.JU. Organizacija bolshikh obemov dannykh v rekomendatelnykh sistemakh podderzhki zhizneobespechenija, vkhodjashhikh v sostav globalnykh platform ehlektronnojj kommercii // XVII International Conference DAMDID/RCDL-2015. Data Analytics and Management in Data Intensive Domains. October 13−16, 2015. Obninsk. Russia. P. 119−124. http://ceur-ws.org/Vol-1536/paper18.pdf.
  5. Volkov I.JU., Galakhov I.V. Arkhitektura sovremennojj informacionno-analiticheskojj sistemy. 2003. http://citforum.ru/con­sulting/BI/ias/.
  6. Voroncov K.V. Statisticheskijj analiz dannykh, kurs lekcijj. VMK MGU. 2016. http://www.machinelearning.ru/ wiki/index.php-title=Statisticheskijj_analiz_dannykh_(kurs_lekcijj%2C_K.V.Voroncov).
  7. Voroncov K.V. Mashinnoe obuchenie, kurs lekcijj. VMK MGU. 2016. http://www.machinelearning.ru/wiki/index.php - title=Mashinnoe_obuchenie_(kurs_lekcijj%2C_K.V.Voroncov).
  8. Belov V.S. Informacionno-analiticheskie sistemy. Osnovy proektirovanija i primenenija. Ucheb. posobie, rukovodstvo, praktikum. M.: Moskovskijj gosudarstvennyjj universitet ehkonomiki, statistiki i informatiki. 2005. 111 s.
  9. http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/syllabus/.
  10. https://www-304.ibm.com/services/learning/ites.wss/zz-en-pageType=course_description&courseCode=DW540G&cc=pe.
  11. Munerman V.I. Postroenie arkhitektur programmno-apparatnykh kompleksov dlja povyshenija ehffektivnosti massovojj obrabotki dannykh // Sistemy vysokojj dostupnosti. 2014. T. 10. № 4. S. 3−16.
  12. Using R Code in Transact-SQL (SQL Server R Services). https://msdn.microsoft.com/en-us/library/mt591996.aspx.
  13. Saternos C. Data Mining Using the RDOM Package. http://www.oracle.com/technetwork/articles/datawarehouse/saternos-r-161569.html.
  14. Duckworth D. IBM Netezza Analytics 2.0 and Big Data. http://www.ibmbigdatahub.com/blog/ibm-netezza-analytics-20-and-big-data.
  15. Munerman V.I. The experience of massive data processing in the cloud using Windows Azure (as an example) // Highly available systems. 2014. V. 10. № 2. P. 3−8.
  16. Alekseeva T.V. Informacionnye analiticheskie sistemy: Uchebnik. M.: Moskovskijj finansovo-promyshlennyjj universitet «Sinergija». 2013. 384 s.
  17. Jenkov J. Service Composition. http://tutorials.jenkov.com/soa/service-composition.html.
  18. The Service Composition. http://serviceorientation.com/serviceorientation/the_service_composition.
  19. Santhosh Kumar Kotte Handling Complex Web Services in InfoSphere Information Server through DataStage ASB Packs v2.0. A step-by-step guide. http://www.ibm.com/developerworks/library/ws-complexws/ws-complexws-pdf.pdf.
  20. Implementing complex Web services. http://searchsoa.techtarget.com/answer/Implementing-complex-Web-services.
  21. In-Database Advanced Analytics for SQL Developers (Tutorial). https://msdn.microsoft.com/en-us/library/mt683480.aspx.
  22. Package «forecast». https://cran.r-project.org/web/packages/forecast/forecast.pdf.
  23. Microsoft/PTVS. https://github.com/Microsoft/PTVS.
  24. Anaconda. User Guide. https://docs.continuum.io/anaconda/#user-guide.
  25. Flask. Web development. http://flask.pocoo.org/.
  26. NumPy. http://www.numpy.org/.
  27. Python Data Analysis Library. http://pandas.pydata.org/.
  28. Machine Learning in Python. http://scikit-learn.org/stable/.
  29. Tosi S. Matplotlib for Python Developers. Packt Publishing. 2009. 308 p.