Distributed Big Data Analytics in Smart Manufacturing

In this program, the main components of a smart factory and enterprise are discussed along with the concepts of Big Data analytics (e.g., AI, ML, DL), distributed computing (e.g., IoT, edge, fog and cloud computing), and data-driven application systems and solutions (e.g., DDDAS, PdM) as it applies to the design and operation of the new generation manufacturing facilities.

QUICK DETAILS

Dates August 2020 Location Zadar
Tuition fee 750 Euro Duration Two weeks (25-35 contact hours)
Field of knowledge Big Data, Industry 4.0, AI Language English
Kind of exams Project Work Level Undergraduate, Graduate, Professional

School Description

Smart factories make it possible to use a large amount of data for development, decision making and manufacturing problem mitigation. To solve analytical problems in this area, we need knowledge about the factory structure and about new ICT and data technologies. Methods of machine learning (ML), deep learning (DL) and artificial intelligence (AI) provide the possibility to predict process breakdowns, reduce product defects.

Last advances in Big Data have opened new opportunities for industrial process optimization. In the manufacturing sector, these advances are accompanied with changes in computing infrastructure. More and more processing is moving out of the servers to the edge, with data flowing between the cloud and the edge only when needed.

All kind of IoT devices, from sensors to industrial robots generate the vast quantities of data. Cloud-based data analytics allow manufacturers to gain valuable insights from this data. Moving that data analysis to the edge and/or fog allows to access to a larger amount of data and to act on that insight promptly. Doing real-time analytics at the edge means that when something unusual happens, manufacturer can take preventive measures before the operational anomalies lead to product defects.

This helps reduce unanticipated maintenance downtime and saves the enterprise money. These days, manufacturers want to have the computing power of a server but in a smaller form factor that could be used on the edge of their shop floor networks. There exists a clear demand of an edge-to-cloud continuum of how much processing you perform at the edge/fog versus how much takes place in the cloud, where manufacturers can work with their data. This flexibility will become ever more
important as edge/fog computing matures.

In this program, the main components of a smart factory and enterprise are discussed along with the concepts of Big Data analytics (e.g., AI, ML, DL), distributed computing (e.g., IoT, edge, fog and cloud computing), and data-driven application systems and solutions (e.g., DDDAS, PdM) as it applies to the design and operation of the new generation manufacturing facilities.

MEET THE LECTOR
Prof. Peter Panfilov
Prof. Peter Panfilov

Prof. Peter Panfilov
Dr Peter Panfilov holds positions as a professor in the Department of innovation and business in IT and in the Department of Software Engineering at National Research University – Higher School of Economics (HSE) in Moscow, Russia. He holds a doctoral degree in information processing and control systems from Russian Academy of Sciences; Institute of Control Sciences and a Diploma in electrical engineering from Moscow Institute of Electronic Machine-Building (MIEM). Prof Panfilov has a long term involvement in computer science and engineering research and development, education and Training.
Download CV

MEET THE LECTOR
Anna Chuvilina Program manager
Anna Chuvilina Program manager

Anna Chuvilina Program manager
Anna graduated from Master’s degree program in Big Data Systems offered by the National Research
University – Higher School of Economics (HSE) of Moscow, Russia. She works as Program manager at
Yandex, Russia’s biggest technology company specializing in Internet-related products and services.

She had various experience in project management, educational and research projects, scientific work
connected with programming and machine learning. Also, she teaches courses in algorithms and data
structures in the Data Science and Business Analytics program of the Faculty of Computer Science at the
HSE.
Download CV

Week 1

  Mon. 1.08. Tue. 2.08. Wed. 3.08. Thu. 4.08. Fri. 5.08.
9.00
-
11.00
Orientation Session: Goals Setting and Project description Basics of Lean Manufacturing: Production process from A till Z From traditional to intelligent and self-organizing production systems History of Production The Way of Effective Presentation: CV, Elevator Pitch, Oral Presentation
11.00
-
12.00
Break Break Break Break Break
12.00
-
14.00
City Rally Level Project Work Project Work Project Work

Week 2

  Mon. 1.08. Tue. 2.08. Wed. 3.08. Thu. 4.08. Fri. 5.08.
9.00
-
11.00
Orientation Session: Goals Setting and Project description Basics of Lean Manufacturing: Production process from A till Z From traditional to intelligent and self-organizing production systems History of Production The Way of Effective Presentation: CV, Elevator Pitch, Oral Presentation
11.00
-
12.00
Break Break Break Break Break
12.00
-
14.00
City Rally Level Project Work Project Work Project Work

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Distributed Big Data Analytics in Smart Manufacturing

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