Магистратура
2020/2021
Аналитика больших данных в индустриальном интернете
Статус:
Курс по выбору
Направление:
38.04.05. Бизнес-информатика
Кто читает:
Департамент бизнес-информатики
Где читается:
Высшая школа бизнеса
Когда читается:
2-й курс, 1, 2 модуль
Формат изучения:
без онлайн-курса
Преподаватели:
Ковалев Илья Александрович,
Сонных Максим Владимирович
Прогр. обучения:
Системы больших данных
Язык:
английский
Кредиты:
6
Контактные часы:
48
Course Syllabus
Abstract
“Big Data Analytics in Industrial Internet” is an elective course taught in the 2d year of the master’s program. The course is designed to give students an overview of an industrial environment as a source of data and related techniques of big data analytics. The duration of the course covers two modules. The course is taught in English and worth 6 credits.
Learning Objectives
- The present course is to introduce students to the core concepts of Big Data Analytics in Industrial Internet.
- Course gives an overview of a industrial applications with BD analytical approach
- The complete technological stack for Machine Data Collection up to cloud analytics
Expected Learning Outcomes
- Know the fundamental concepts, principles and approaches to description of the Big Data Landscape in Industry
- Be able to understand the main problems of the Big Data Analytics in Industry, get acquainted to the architectural components and programming models used for scalable data analysis
- Learn how to use one of the most common frameworks and tools
Course Contents
- Industrial revolutions
- 4th Industrial revolution. Features, drivers and challenges
- Industry 4.0. Definition, components, design principles
- Data Analytics. Manufacturing Analytics
- Sources of data in industrial environment
- IoT Gateway: collecting low-level shopfloor data
- Smart Factory
- Data analytics concepts
- Data analytics methodologies and architectures
- Data analytics tools and platforms application in industry
- Industrial use cases
- SQL and noSQL databases
- CAP theorem, eventual consistency
- HBASE: architecture, core work principles
- Reference architectures in Industry 4.0
- RAMI 4.0 – The Reference Architectural Model for I4.0
- National and alternative reference architectures
- Criteria for I4.0 products
Interim Assessment
- Interim assessment (2 module)0.2 * Activity during classes + 0.5 * Exam + 0.3 * Home task
Bibliography
Recommended Core Bibliography
- Lin, J., & Dyer, C. (2010). Data-Intensive Text Processing with MapReduce. Morgan & Claypool Publishers.
- White T. Hadoop: The Definitive Guide. - O'Reilly Media, 2015.
Recommended Additional Bibliography
- Buyya, R., Calheiros, R. N., & Vahid Dastjerdi, A. (2016). Big Data : Principles and Paradigms. Cambridge, MA: Morgan Kaufmann. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1145031