Keynote Speakers

Prof. dr. ir. Michael (Mike) P. Papazoglou

Chair of Computer Science & Executive Director
of the European Research Institute
in Services Science Tilburg University, the Netherlands

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Abstract: Industry 4.0 represents the radical change that is shaking the foundations of the automation and manufacturing industry landscape today. Industry 4.0 technologies are building on many strategic ideas, concepts, approaches, systems, and structures that promise to improve a manufacturer's flexibility and speed, enabling more individualized products, efficient and scalable production, and a high variance in production control. Increased connectivity and sophisticated data-gathering and analytics capabilities enabled by smart technologies and the Internet of Things (IoT) makes it possible to build smarter products, manufacturing processes, and even end-to-end production and factory ecosystems.

To fully realize the promise of Industry 4.0, disparate systems, devices, data and processes need to connect, communicate, and interoperate. This talk will focus on a novel programming paradigm and a flexible programming environment for production processes that helps developers develop design-to-production industrial automation solutions by employing structured higher-level modular software techniques. Programmable abstraction methods for modules cater for managing complexity and enabling integration of data, processes, devices and resources from plant operation to supply chain.

Short bio: Michael P. Papazoglou is a highly acclaimed academic with noteworthy experience in areas of education, research and leadership pertaining to computer science, information systems, industrial engineering and digital manufacturing. He is the executive director of European Research Institute in Service Science and holds the Chair of Computer science at Tilburg University, the Netherlands. He is noted as one of the original promulgators of ‘Service-Oriented Computing’ and was the scientific director of the acclaimed European Network of Excellence in Software Systems and Services (S-CUBE). He is renowned for establishing local ‘pockets of research excellence’ in service science and engineering in several European countries, China, Australia and the UAE. Papazoglou is an author of the most highly cited papers in the area of service engineering and Web services worldwide with a record of publishing 32 (authored and edited) books, and over 200 prestigious peer-refereed papers along with approx. 18,000 citations (H-index factor 52). He holds distinguished/honorary professorships at 11 universities around the globe. He has delivered over 45 keynote addresses since 2000 and chaired 12 prestigious international peer refereed conferences. Papazoglou is the founder and editor-in-charge of the MIT Press book series on Information Systems as well as the founder and editor-in-charge of the new Springer-Verlag book series on Service Science.

Prof. Dr. Knut Hinkelmann

Head of MSc in Business Information Systems
FHNW University of Applied Sciences and Arts Northwestern Switzerland

Title: Digitalization of Knowledge Work Processes

Abstract: The presentation covers three aspects of digitalizing knowledge work: The process flow, the digitalization of process flow, digitalization of knowledge tasks and the agility to react on changes and to deal with unforeseen situations.

Process logic can be categorized by its degree of structure. For structured business processes the tasks and the criteria of their execution can be determined in advance. This is supported by process modelling languages like BPMN. Case management, however, deals with processes, for which not all tasks are known and for which the flow cannot be determined in advance. While OMG has developed CMMN as a standard modelling language for case management, I will argue that a combination of structured processes and cases is more appropriate.

Machine Learning and Knowledge Engineering are regarded as two complimentary approaches for automation knowledge tasks. DMN is a modelling language for engineering decision logic. While, knowledge engineering is appropriate for representing expert knowledge, which people are aware of and that has to be considered for compliance reasons or explanations. On the other hand, machine learning helps to solve complex tasks based on real-world data instead of pure intuition. It is most suitable for building AI systems when knowledge is not known, or knowledge is tacit.

Modeling can make the knowledge explicit and allows different stakeholders to communicate about the knowledge. Knowledge changes with experience and needs to be adapted to changing environments. Agile modelling approaches allow domain experts to create appropriate to create domain-specific modelling languages on the fly.

Short bio: Knut Hinkelmann is Head of the Master of Science in Business Information Systems at the FHNW University of Applied Sciences and Arts Northwestern Switzerland. He also is adjunct professor at the University of Camerino, Italy, and research associate at the University of Pretoria, South Africa. In 1988 he obtained a diploma in Computer Science and in 1995 a PhD from the University of Kaiserslautern.

After the study he worked for the Research Institute for Applied Knowledge Processing (FAW). Then he was researcher and head of the Knowledge Management research group at the German Research Center for Artificial Intelligence (DFKI). After having worked as product manager for Insiders Information Management GmbH, he joined FHNW in August 2000 as a professor for Information Systems. He has been head of Bachelor and Master programs and has done application-oriented research in areas like knowledge representation and reasoning, knowledge management, and business process management.

Prof. Mikhail Yu. Kataev

Department of Control Systems
Tomsk State University of Control Systems and Radioelectronics

Title: Big Data in the Information Systems of Enterprise Management


In the era of supercomputers, the Internet, mobile communications, solutions such as Big Data, business intelligence, cloud computing, data mining and business intelligence systems play an important role in the management of various organizations. Such decisions have a great influence on the level of development of small and large organizations, and are an indispensable factor in the development of individual entrepreneurship. Currently, virtually all information generated by various information software systems is transmitted over the Internet and is growing day by day.  Such large amounts of data can serve as a basis for the preparation and conduct of various analyzes useful for the management of the enterprise at all its levels (operational, tactical and strategic).  Big Data is a term associated with processing technology and comprehensive analysis of huge amounts of data that can be static or obtained in real time. Such data can arise not only in firms such as Google, Amazon or Facebook, but also in smaller companies. The concept of Big Data communicates with 4Vs, when all the "V" define the main parts of the technology: Volume (Data Size), Velocity (The speed at which data is received and with which data must be processed), Diversity (processing and analysis techniques should focus on two aspects : syntax and semantics) and Value (new data sources should be perceived by existing programs). With the advent of new software, hardware and telecommunications solutions, Big Data received considerable attention from the developers of enterprise information systems (EIS). The technological aspect of the application of large data involves the consideration of technologies for the collection, storage, processing and analysis and visualization of data. Some of these technologies have existed for a long time, while others must be adapted to Big Data.  Currently, many enterprises use business processes in their activities, however, changes in the external and internal environment lead to the need to change them. The data that are generated by business processes play a central role in EIS. Business data is necessary for business logic in understanding the role of the enterprise in the external environment and management, by studying the internal environment of the enterprise. The management tasks use decision support systems (DSS), computer solutions that can be used to study the activities of the enterprise and support integrated decision-making. The study of activities is connected with monitoring in real time mode for tracking business events and obtaining their quantitative characteristics. The report provides information on the direction of Big Data that are related to business processes and the direction of their application in storage, processing and analysis tasks, decision support, the formation of advisory systems. We propose the architecture of integration of large data for the analysis of events of the state institution that will help the management to timely analyze the results of the implementation of business processes and evaluate the work of each employee.

Short bio: TBA


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