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Applying e-learning systems for big data education

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Języki publikacji
EN
Abstrakty
EN
Processing massive data amounts and Big Data became nowadays one of the most significant problems in computer science. The difficulties with education on this field arise, the appropriate teaching methods and tools are needed. The processing of vast amounts of data arriving quickly requires the choice and arrangement of extended hardware platforms. In the paper we will show an approach for teaching students in Big Data and also the choice and arrangement of an appropriate programming platform for Big Data laboratories. Usage of an e-learning platform Moodle, a dedicated platform for teaching, could allow the teaching staff and students an improved contact with by enhancing mutually communication possibilities. We will show the preparation of Hadoop platform tools and Big Data cluster based on Cloudera and Ambari. The both solutions together could enable to cope with the problems in education of students in the field of Big Data.
Rocznik
Strony
85--96
Opis fizyczny
Bibliogr. 23 poz., rys.
Twórcy
autor
  • Faculty of Computer Science, Electrical Engineering and Automatics University of Zielona Góra (UZ)
autor
  • Faculty of Mathematics, Computer Science and Econometrics University of Zielona Góra (UZ)
autor
  • Faculty of Mathematics, Computer Science and Econometrics University of Zielona Góra (UZ)
Bibliografia
  • [1] T. White (2015) Hadoop: The Definitive Guide, 4th Edition, O’Reilly Media, Inc (polish edition by Helion, Gliwice, 2016).
  • [2] M. Tabakow, J. Korczak, B. Franczyk (2014) Big Data − definitions, challenges and information technologies, BUSINESS INFORMATICS 1(31) (in Polish).
  • [3] EMC Education Services – Editor (2015) Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, John Wiley and Sons, Inc., Indianapolis, Indiana.
  • [4] W. H. Rise (2008) Moodle 1.9 e-learning course development: a complete guide to successful learning using Moodle 1.9, Packt Publishing Ltd., Birmingham, UK (polish edition by Helion, Gliwice 2010).
  • [5] R. Jurney (2013) Agile Data Science: Building Data Analytics Applications with Hadoop, O’Reilly Media, Inc. (polish edition by Helion, Gliwice, 2015).
  • [6] M. Grzenda, J. Legierski (2017) Databases, data warehouses, Big Data platforms – variety of needs and solutions, Data Science Summit 2017.
  • [7] The Ubuntu Manual Team (2014) Getting Started with Ubuntu 14.04, Second Edition, http://ubuntu-manual.org/, 12-11-2015.
  • [8] The Apache Hadoop Webpage, http://hadoop.apache.org/, 20-06-2017.
  • [9] The Ubuntu Webpage, https://www.ubuntu.com/, 07-03-2017.
  • [10] The Moodle Webpage, https://moodle.com/, 27-05-2017.
  • [11] Large Hadron Collider, http://opendata.cern.ch/, 01-09-2017.
  • [12] National Centers for Environmental Information, National Oceanic and Atmospheric Administration Webpage, http://www.noaa.gov/, 20-09-2016.
  • [13] The Oracle VM VirtualBox Page, https://www.virtualbox.org/, 20-04-2017.
  • [14] The VMWare Page, https://www.vmware.com/, 20-04-2017.
  • [15] The QEmu Page, https://www.qemu.org/, 20-04-2017.
  • [16] The Cloudera Webpage, https://www.cloudera.com/, 02-02-2017.
  • [17] The Apache Ambari Webpage, https://ambari.apache.org/, 20-06-2017.
  • [18] The Amazon Web Services, https://aws.amazon.com/, 15-092017.
  • [19] The Microsoft Azure, https://azure.microsoft.com/, 15-09-2017.
  • [20] B. Marr (2015) Why only one of the 5 Vs of big data really matters, IBM BigData and Analitic Hub, http://www.ibmbigdatahub.com/blog/why-only-one-5-vs-big-datareally-matters, 30-04-2017
  • [21] T. Shafer (2017) The 42 V's of Big Data and Data Science, Elder Research, https://www.elderresearch.com/company/blog/42-v-of-big-data, 30-04-2017
  • [22] Oracle Database 12c SQL Language Reference, https://docs.oracle.com/database/122 /SQLRF/toc.htm, 30-05-2017
  • [23] S. Robak, B. Franczyk, M. Robak (2014) Research Problems Associated with Big Data Utilization in Logistics and Supply Chains Design and Management, Annals of Computer Science and Information Systems, Volume 3.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
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