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Master 2019/2020

Big Data Collection, Storage&Processing in Heterogeneous Distributed Computer Networks

Type: Elective course (Big Data Systems)
Area of studies: Business Informatics
Delivered by: Department of Innovation and Business in Information Technologies
When: 1 year, 2, 3 module
Mode of studies: offline
Master’s programme: Big Data Systems
Language: English
ECTS credits: 5

Course Syllabus

Abstract

The course covers areas related to massive datasets stored in the cloud or generated from embedded systems and from the Internet of Things (IoT), how data is stored and utilized within distributed systems of enterprise and how organizations can utilize data to change and improve business processes.
Learning Objectives

Learning Objectives

  • The general goal of the course is to prepare graduates for effective performance of the managerial role of collection, storage and processing of the big data, work in team and to be able to further commercialize collected data
Expected Learning Outcomes

Expected Learning Outcomes

  • Understand the actual problems connected to the big data collection, storage&processing.
  • Know different sources of big data and how that data are processed and stored.
  • Know major business-models which might be used for big data after processing.
  • Know basic principles of developing and managing systems to collect, store and process big data.
  • Know different sources of big data and how that data are processed and stored
Course Contents

Course Contents

  • Introduction to IoT. Big Data in IoT. Challenges and open issues in IoT.
    At this topic students would understand what is the Internet of Things, connections between IoT and Big Data. Why IoT is one of the major sources of Big Data. There will be information about the challenges and open issues in IoT especially when we are talking about data storages and when we are talking about real-time processing of the collected data. Examples from the real business and discussions with the students about future of the IoT and Big Data will also be a part of the topic.
  • Sensor networks and machine-to-machine (m2m). Standardization, relevant usage models, business use cases and ROI
    Within this topic the students will learn the principles of machine-to-machine interaction, correspondent technical challenges, network architectures standardized by ETSI and ITU. The special attention will be given to m2m and conventional operators and service providers, their new demands in m2m business, ways to generate revenues out of m2m. Case studies from different industries will be also provided and analyzed
  • Smart Grids.
    Within this topic the students will understand the challenges for Smart Grid and general impact of the technololgy when implemented. The special attention will be given to the challenges of the Internet of Energy, Smart Grid communication standards, interoperability concepts as well as up-to-date status of EU implementation of Smart Grids.
  • Short-range wireless technologies: data collections, processing and storage
    Within this topic the students will learn the place of the short-range (capillary) wireless technologies in IoT and role in Big Data collection. The particular attention will be given to the standard technologies such as 6LoWPAN, IEEE 802.11 and .15 and their key features persistent to the Big Data collection and transfer.
  • Cellular technologies: data collections, processing and storage
    Within this topic the students will learn the general principles of the cellular technologies and their evolution towards 5G finally allowing implementation of IoT concept and handling of Big Data. A special attention will be given to understanding of network architectures and network capacity problem.
  • 5G telecommunication networks and systems
    Within this topic the students will learn the cellular network concept that is currently under development till 2020. The special attention will be given to challenges for 5G and driving services and applications such as Big Data timely delivery
  • Data storages. Data processing techniques. Open Data concept.
    Within this topic the students will learn different types of data storages and open data concept, how different open data sources are used in business and what is the key point of open data.
  • Use cases, service implementations and business opportunities for operators.
    Within this topic the students will learn the challenges network operators have been facing with introduction of Big Data and related infrastructure to collect and store it. The special attention will be given to conventional operator’s business models, correspondent CAPEX and OPEX.
  • Mobile applications as a source of data (Mobile commerce and key issues).
    Within this topic the students will learn how mobile applications generate data, where they keep it, how it is collected and for which purposes it is used.
  • Additional topics of the course
    Additioal topics based on students expectations will be covered
Assessment Elements

Assessment Elements

  • non-blocking Classes presentations
  • non-blocking Essay
  • non-blocking hometask
  • non-blocking Examination
    asynchronous proctoring
  • non-blocking Regulations for mid-term examination with asynchronous proctoring.
    Regulations for mid-term examination with asynchronous proctoring. 3. During the exam. 3.1 The student is obliged: - do not leave the view area of the webcam during the examination; - do not mute the microphone or reduce its sound sensitivity during the examination; - use only one display medium (monitor, TV, projector), one keyboard, one manipulator (computer mouse, TrackPoint, etc.); - focus on task performance (computer screen or task sheets) without taking your eyes away for a long time (15 seconds or more). Except for instruments that are allowed to be used while taking the exam (this information should be recorded in the commentary to the examination rules for a particular course of study). 3.2 The student is not allowed: - obtain the assistance of third parties during the examination; - provide access to a computer to unauthorized persons during the exam; - engage in conversations with third parties; - use reference materials (books, cheat sheets, paper and electronic records), any gadgets (cell phones, pagers, tablets), additional monitors and computer equipment, except that used for the exam, open the browser tabs (Yandex, Google, etc.). 3.3 The detection of unauthorized teaching and learning materials, electronic media, and violations of these rules is grounds for a decision to terminate the exam and give an "unsatisfactory" grade ("0" on a ten-point scale) whether or not they were used in the exam. 3.4 The student is allowed: - use a glass of water/juice - use tools for solving the examination tasks, if provided for in the rules for the examination (see para. 2.1); - continue to take the examination tasks in the case of short-term communication disruptions (total duration not exceeding 5 minutes during the examination). 4. Technical requirements 4.1 The student must get acquainted with the requirements to the user's PC: 4.1.1 Stationary computer or notebook (mobile devices are not supported); 4.1.2 Windows operating system (versions 7, 8, 8.1, 10) or Mac OS X Yosemite 10.10 and higher; 4.1.3 Internet browser Google Chrome is the latest version at the time of the exam (to install the browser, use the link https://www.google.com/chrome/, to check and update the version of the browser, use the link chrome://help/, you can see the version number of your browser and the button to update if available) or Yandex Browser is the latest version; 4.1.4 Data transfer via network ports is allowed: 80 TCP, 443 TCP, 3478 TCP/UDP (check with ISP/open control panel - system and security - Windows-assistant firewall additional parameters. Make sure there are no restrictions on incoming and outgoing connections). 4.1.5 Web camera (including built-in notebooks) in good working order and switched on; 4.1.6 Microphone (including built-in notebooks) in good working order and turned on; 4.1.7 Permanent Internet connection with a data rate of at least 5 Mbit/sec from the user; mobile Internet is not recommended due to the high probability of technical failures and interruptions in the broadcast of the exam;
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    0.2 * Classes presentations + 0.2 * Essay + 0.4 * Examination + 0.2 * hometask
Bibliography

Bibliography

Recommended Core Bibliography

  • Computer Networks : A Systems Approach. (2019). Princeton, New Jersey: Larry Peterson and Bruce Davie. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsotl&AN=edsotl.OTLid0000771
  • Vermesan, O., & Friess, P. (2016). Digitising the Industry : Internet of Things Connecting the Physical, Digital and Virtual Worlds. [N.p.]: River Publishers. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1800544

Recommended Additional Bibliography

  • Elk, K. (2019). Embedded Software for the IoT (Vol. Third edition). Boston: De|G Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1975740
  • Hurwitz, J., Kaufman, M., Halper, F., & Nugent, A. (2013). Big Data For Dummies. Hoboken, N.J.: For Dummies. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=565511