Bio: Dirk Draheim received the PhD from Freie Universität Berlin and the habilitation from University of Mannheim, Germany. Currently, he is full professor of information society technology at Tallinn University of Technology and head of the Information Systems Group, Tallinn University of Technology, Estonia. The Information Systems Group conducts research in large and ultra-large-scale IT systems. He is also an initiator and leader of numerous digital transformation initiatives. Dirk is author of the Springer books "Business Process Technology", "Semantics of the Probabilistic Typed Lambda Calculus" and "Generalized Jeffrey Conditionalization", and co-author of the Springer book "Form-Oriented Analysis".
Affiliation: Tallinn University of Technology, Estonia
Research interests: design and implementation of large-scale information systems
Keynote Topic: Digital Government Ecosystems: Foundations, Architecture, Implementation
Abstract: The so-called digital transformation is perceived as the key enabler for increasing wealth and well-being by politics, media and the citizens alike. In the same vein, digital government steadily receives more and more attention. Digital government gives rise to complex, large-scale state-level system landscapes consisting of many players and technological systems, i.e., digital government ecosystems. In this talk, we systematically approach the state-level architecture of digital government ecosystems. As a case study, we look into the case of e-Estonia and its data exchange layer X-Road. We will discover the primacy of the state's institutional design in the architecture of digital government ecosystems. Based on that insight, we will establish the notion of data governance architecture, which links data assets with accountable organizations. Our investigation results into a digital government architecture framework that can help in large-scale digital government design efforts. With its focus on data, the proposed framework perfectly fits the current discussion on moving from ICT-driven to data-centric digital government.
Bio: Václav Snášel’s research and development experience includes over 35 years in the Industry and Academia. He works in a multi-disciplinary environment involving Artificial Intelligence, Bioinformatics, Information Retrieval, Machine Intelligence, Data Science, Nature and Biologically Inspired Computing, and applied to various real-world problems. He studied Numerical Mathematics at Palacky University in Olomouc, a PhD degree obtained at Masaryk University in Brno. From 2001 to 2009, he worked as a researcher at The Institute of Computer Science of the Academy of Sciences of the Czech Republic. Since 2009 he has worked as Head of the research program Knowledge Management at IT4Innovation National Supercomputing Center; from 2010 to 2017, he worked as the Dean of the Faculty of Electrical Engineering and Computer Science, and from 2017 he is Rector of VSB-Technical University of Ostrava. He teaches as a professor at VSB – Technical University of Ostrava.
He has given 18 plenary lectures and conference tutorials in these areas. He has authored/coauthored several refereed journal/conference papers and book chapters. He has published more than 700 papers (520 papers are indexed at Web of Science, 700 indexed at Scopus). He has supervised many PhD students from the Czech Republic, Jordan, Yemen, Slovakia, Ukraine, Russia, India, China, Lybia, and Vietnam. He also supervised postdoc students from the Slovak Republic, Uruguay, and Egypt. He is co-editor of 40 books in Springer. He is the founder of successful conference series: Euro- China conference (Shen Zhen 2014, Ostrava 2015, Fujian 2016, Malaga 2017, Xi’an 2018) and Afro- Euro Conference (Addis Ababa 2014, Paris 2015, Marrakesh 2016).
Affiliation: VSB - Technical University of Ostrava, Czech Republic
Keynote Topic: In-Memory Computing Architectures for Big data and Machine Learning Applications
Abstract: Traditional computing hardware is working to meet the extensive computational load presented by the rapidly growing Machine Learning (ML) and Artificial Intelligence algorithms such as Deep Neural Networks and Big Data. In order to get hardware solutions to meet the low-latency and high-throughput computational needs of these algorithms, Non-Von Neumann computing architectures such as In-memory Computing (IMC) have been extensively researched and experimented with over the last five years. This study analyses and reviews works designed to accelerate Machine Learning. We investigate different architectural aspects and directions and provide our comparative evaluations. We further discuss IMC research's challenges and limitations and present possible directions.
Affiliation: Sungkyunkwan University, Korea
Keynote Topic: Research Issues for Federate Learning in Smart Medical Systems
Since the Federated learning, which makes AI learning possible without moving local data around, was introduced by google in 2017 it has been actively studied particularly in smart medical area. In fact, the idea of learning in AI system without collecting data from local systems is very attractive because data remain in local sites. However, federated learning techniques still have various open problems because of the characteristics of federated learning such as distribution, participating clients and vulnerable environments.
In this presentation, the current issues to make federated learning flawlessly useful in real world will be briefly overviewed. They are related to malicious client detection, asynchrony, data/system heterogeneity, and integrating learning models. Also, we introduce the framework, we currently develop, to experiment various techniques and protocols to find solutions for the issues. The framework will be open to public after development.
Bio: Johann Eder is full professor for Information and Communication Systems in the Department of Informatics-Systems of the Alpen-Adria Universität Klagenfurt, Austria. From 2005-2013 he served as Vice President of the Austrian Science Funds (FWF). He held positions at the Universities of Linz, Hamburg and Vienna and was visiting scholar at AT&T Shannon Labs, NJ, USA, the University of California Santa Barbara, CA, USA, and the New Jersey Institute of Technology, NJ, USA.
Johann Eder published more than 190 papers in peer reviewed international journals, conference proceedings, and edited books. He chaired resp. served in numerous program committees for international conferences and as editor and referee for international journals.
Affiliation: University of Klagenfurt, Austria
Research interests: The research interests of Johann Eder are information systems engineering, business process management, and data management for medical research. A particular focus of his work is the evolution of information systems and the modelling and management of temporal information and temporal constraints. Another focus is the application of information technology for medical research in particular information systems for biobanking. He successfully directed numerous competitively funded research projects on workflow management systems, temporal data warehousing, process modelling with temporal constraints, application interoperability and evolution, information systems modelling, information systems for medical research, etc.