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2024
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Title : Regional Innovation On Industrial Digital Transformation |
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Time : 17:00 Feb. 05 (Mon), 2024 |
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Speaker : Prof. KWON YEONG-IL, PhD. Head of Graduate School of Management of Advanced Technology, Hoseo University, Korea.
He is currently a Professor at the Graduate School of Industrial Digital Transformation, Hoseo University, South Korea. He received the M.Sc. degree from the Department of Computer Science, KAIST (Korea Advanced Institute of Science and Technology) in 1998 and the Ph.D. degree in the Department of IT Service Management from Soongsil University in 2015.
Dr. Kwon made a lot of contributions to the national ICT development while serving as a General Director at the National Information Society Agency, so called NIA for implementing e-Government, Big data and AI, Open Government Data for many years. He was also a Member of the Big data Policy Committee on National Tax Agency and National Statistics Agency. He has published many books and articles on Big data, Global management of technology, etc. His research interests include big data, platform business, mobile edge computing and smart factory
Abstract : Digital Transformation (DX) requires establishing a strategic direction to innovate both public and private sectors along with the development of artificial intelligence technology. Industrial digital transformation requires local innovation that applies intelligent technology, especially to the factories of semiconductor, automobile, and manufacturing companies. Hoseo University is located in Chungnam Province which has many industrial complexes for small and medium-sized businesses. As one of the cutting-edge technologies in the manufacturing field, cloud computing and edge computing must be implemented using 5G-based MEC (Mobile Edge Computing) technology. Hoseo University is applying the ABL (Actual-Task Base Learning) model to solve technical issues in small and medium-sized companies.
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Title : Gradual Process Mining |
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Time : 17:00 Feb. 05 (Mon), 2024 |
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Speaker : Prof. Kwanghoon Pio Kim, Ph.D.
He is a Full Professor in the Division of AI Computer Science and Engineering as well as the Founder and Supervisor of the Data and Process Engineering Research Laboratory, Kyonggi University, South Korea. At Kyonggi University, he had been the Dean of the Computerization and Informatics Institute from 2017 to 2021; Since 2007, he has been in charge of the Founder and the Director of the Contents Convergence Software Research Institute, where he has been leading and fulfilling a multi-million-dollar research project that will be continuously supported and funded by the National Research Foundation of Korea, from 2020 to 2029, ever since the institute was officially designated as the National Science and Engineering Research Institute by the Ministry of Education, South Korea, in 2020. He received B.S. degree in computer science from Kyonggi University in 1984. And he received M.S. degree in computer science from Chungang University in 1986. He also received his M.S. and Ph.D. degrees from the computer science department at the University of Colorado Boulder, in 1994 and 1998, respectively. He had worked as researcher and developer at Aztek Engineering, American Educational Products Inc., and IBM in USA, as well as at Electronics and Telecommunications Research Institute (ETRI) in South Korea. In present, he is fulfilling the presidency of the KSII (Korean Society for Internet and Information) from 2023 to 2024. He has been in charge of a country-chair (Korea) and ERC vice-chair of the Workflow Management Coalition.
Abstract : The workflow (and business process) literature has been looking for efficient redesigning and reengineering approaches because of not only that workflow and business procedures are becoming massively large-scaled and complicated, but also, nevertheless, that their life-cycle and recycling (reengineering) periods are becoming swiftly shorter. As depicted in the figure, the business process life-cycle management with business process redesign, reengineering, and restructuring approaches is needed to be supported by a process mining system performing the process discovery and rediscovery functionality. The process mining system is based upon the workflow and business process enactment audit and event history traces logged and collected at the runtime of workflow and business process management systems. In the diagnosis phase of the process life-cycle management, we need a way to reasonably discover the discrepancy between the original process model as it is built and the enacted process as it is actually executed. The discrepancy can be discovered straightforwardly by the process mining system and eventually become the very valuable knowledge to redesign, reengineer, and reconstruct the original process model for the sake of managing its life-cycle effectively and efficiently.
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