Сбор, хранение и обработка данных в гетерогенных распределенных компьютерных сетях

Магистратура 2020/2021

Статус: Курс по выбору

Направление: 38.04.05. Бизнес-информатика

Кто читает: Департамент бизнес-информатики

Где читается: Высшая школа бизнеса

Когда читается: 1-й курс, 2, 3 модуль

Формат изучения: без онлайн-курса

Преподаватели: Кучерявый Евгений Андреевич

Прогр. обучения: Системы больших данных

Язык: английский

Кредиты: 5

Контактные часы: 40

Full 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

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

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

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

Classes presentations
Essay
Hometask
Examination

Interim Assessment

Interim assessment (3 module)
0.2 * Classes presentations + 0.2 * Essay + 0.4 * Examination + 0.2 * Hometask

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

Course Syllabus