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Keynote Lectures

Decision Guidance Systems and Applications to Manufacturing, Power Grids, Supply Chain and IoT
Alexander Brodsky, George Mason University, United States

Empirical Approach to Learning from Data (Streams)
Plamen Angelov, Lancaster University, United Kingdom

Available Soon
Salvatore Distefano, Università degli Studi di Messina, Italy

The Future of Information Systems - Direct Execution of Enterprise Models, Almost Zero Programming
David Aveiro, University of Madeira / Madeira-ITI, Portugal

 

 

Decision Guidance Systems and Applications to Manufacturing, Power Grids, Supply Chain and IoT

Alexander Brodsky
George Mason University
United States


Brief Bio
Alex Brodsky is Professor in the department of Computer Science and Director of the Masters of Science Degree in Information Systems at George Mason University. He teaches classes in Database Management and Decision Guidance Systems, graduated 15 PhD students and currently advises other four. Alex’s current research interests include Decision Support, Guidance and Optimization (DSGO) systems; and DSGO applications, including to Energy, Power, Manufacturing, Sustainability and Supply Chain. He earned his Ph.D. and prior degrees in Computer Science and/or Mathematics from the Hebrew University of Jerusalem.
Alex has published over 115 refereed papers, including five that received Best Paper Awards, in scholarly peer-reviewed journals, books and conference/workshop proceedings. For his research work related to DSGO systems, Alex received a National Science Foundation (NSF) CAREER Award, NSF Research Initiation Award, and funding from the Office of Naval Research (ONR), National Aeronautics and Space Administration (NASA), National Institute of Standards and Technology (NIST), and Dominion Virginia Power.
Alex serves/ed in leadership roles in research conferences, including as Conference Chair of IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2017); Program Chair of IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2013); Program Co-chair of the IEEE ICDE workshop on Data-Driven Decision Guidance and Support Systems (DGSS 2012, and DGSS 2013); General Vice Co-chair of the IEEE International Conference on Data Engineering (ICDE 2012); and Conference Chair of the Fifth International Conference on Principles and Practice of Constraint Programming (CP99).
Prior to joining Mason in 1993, Alex worked at IBM T.J. Watson Research Center, at Israel Aircraft Industries and was an R&D officer in the Computer Division of Communications, Electronics and Computer Corps, Israel Defense Forces. He also has start-up and commercialization experience, and is a member of ACM, IEEE, INFORMS and INSTICC.


Abstract
Decision Support Systems (DSS) are widely used to support organizational and personal decision-making in diverse areas such as engineering systems, finance, business, economics and public policy. They are becoming increasingly critical with the information overload from the Internet. While the scope of DSS is broad, Decision Guidance Systems (DGS) are a class of DSS geared to elicit knowledge from domain experts and provide actionable recommendations to human decision-makers, with the goal of arriving at the best possible course of action.
Currently, the practice of building Decision Guidance (DG) Systems resembles developing database applications decades ago before the invention of the relational Database Management Systems (DBMS). DG applications are typically one-off and hard-wired to specific problems; require significant interdisciplinary expertise to build; are highly complex and costly; and are not extensible, modifiable, or reusable. Therefore, a paradigm shift for the development of DG systems is needed. The key idea is to introduce and develop Decision Guidance Management Systems (DGMS), which would allow fast and easily-extensible development of DG applications, similar to easy development of DB applications using DBMS.
In this talk I will overview research toward this goal, including the recently developed Unity DGMS, and exemplify its use in the area of manufacturing, energy and power. I will also discuss ideas on how to use the emerging DGMS technology to translate the potential and multibillion dollar investment in Internet of Things (IoT) into business value, e.g., through (1) better predictability of demand and inventory visibility, (2) better tracking and efficiency of equipment and operating assets (3) accelerated innovation and product support, (4) improved alignment and collaboration among business functions and (5) sustainability and quality through visibility to energy and resource consumption.

 

 

Empirical Approach to Learning from Data (Streams)

Plamen Angelov
Lancaster University
United Kingdom


Brief Bio
Dr Plamen Angelov, is a Reader in Computational Intelligence and coordinator of the Intelligent Systems Research at Infolab21, Lancaster University, UK. He is a Senior Member of the IEEE and Chair of two Technical Committees (TC); TC on Standards, Computational Intelligence Society and TC on Evolving Intelligent Systems, Systems, Man and Cybernetics Society. He is also a member of the UK Autonomous Systems National TC, of the Autonomous Systems Study Group, NorthWest Science Council, UK and of the Autonomous Systems Network of the Society of British Aerospace Companies. He is a very active academic and researcher who authored or co-authored over 150 peer reviewed publications in leading journals (50+) peer-reviewed conference proceedings, a patent, a research monograph, a number of edited books, and has an active research portfolio in the area of computational intelligence and autonomous system modelling, identification, and machine learning. He has internationally recognised pioneering results into on-line and evolving methodologies and algorithms for knowledge extraction in the form of human-intelligible fuzzy rule-based systems and autonomous machine learning. Dr. Angelov is also a very active researcher leading projects funded by EPSRC, ASHRAE-USA, EC FP6 and 7, The Royal Society, Nuffield Foundation, DTI/DBIS, MoD, industry (BAE Systems, 4S Information Systems, Sagem/SAFRAN, United Aircraft Corporation and Concern Avionica, NLR, etc.). His research contributes to the competitiveness of the industry, defence and quality of life through projects, such as the ASTRAEA project - a £32M (phase I and £30M phase II) programme, in which Dr. Angelov led projects on Collision Avoidance (£150K, 2006/08), and Adaptive Routeing (£75K; 2006/08). The work on this project was recognised by 'The Engineer Innovation and Technology 2008 Award in two categories: i) Aerospace and Defence and ii) The Special Award. Other examples of research that has direct impact on the competitiveness of UK industry and quality of life are the BAE Systems-funded project on Sense and Avoid (principal investigator, £66K; 2006/07), BAE funded project on UAS Passive Sense, Detect and Avoid Algorithm Development (£24K consultancy, a part of ASTRAEA-II, 2009), the BAE Systems-funded project (co-investigator, £44K, 2008) on UAV Safety Support, EC-funded project (€1.3M, co-investigator) on Safety (and maintenance) improvement trough automated flight data Analysis, the Ministry of Defence funded projects ('Multi-source Intelligence: STAKE: Real-time Spatio-Temporal Analysis and Knowledge Extraction through Evolving Clustering', £30K, principal investigator, 2011 and Assisted Carriage: Intelligent Leader-follower algorithms for ground platforms, £42K, 2009 which developed unmanned ground-based vehicle prototype taken further by Boeing-UK in a demonstrator programme in 2009-11), so called 'innovation vouchers by the North-West Development Agency-UK and Autonomous Vehicles International Ltd. (£10K, 2010, principal investigator), MBDA-led project on Algorithms for automatic feature extraction and object classification from aerial images (£56K, 2010) funded by the French and British defence ministries. Dr. Angelov is also the founding Editor-in-Chief of the Springer's journal on Evolving Systems and serves as an Associate Editor of several other international journals. He also Chairs annual conferences organised by IEEE, acts as Visiting Professor (2005, Brazil; 2007, Germany; 2010, Spain) regularly gives invited and plenary talks at leading companies (Ford, The Dow Chemical, USA; QinetiQ, BAE Systems, Thales, etc.) and universities (Michigan, USA; Delft, the Netherlands; Leuven, Belgium, Linz, Austria, Campinas, Brazil, Wolfenbuettel, Germany, etc). More information can be found at www.lancs.ac.uk/staff/angelov.


Abstract
The staggering proliferation of heterogeneous, large scale data sets and streams is recognised as an untapped resource which offers new opportunities for extracting aggregated information to inform decision-making in policy and commerce. However, currently existing methods and techniques for data mining involve a lot of prior assumptions, handcrafting and a range of other bottleneck issues: i) scalability – vast amounts of data which require high throughput automated methods (e.g. manual labelling of data samples can be prohibitive); ii) complex, heterogeneous data (including signals, images, text that may be uncertain and unstructured); iii) dynamically evolving, non-stationary data patterns, and the shortcomings of the “standard” assumptions about data distributions; iv) the need to hand craft features, parameters or set thresholds. As a result, a large proportion of the available data remains untapped. The key challenge now is to manage, process and gain value and understanding from the vast quantity of heterogeneous data without handcrafting and prior assumptions, at an industrial scale. In this talk a newly emerging theoretical framework which we call Empirical Data Analytics will be introduced and described and its relation to the probability, density, centrality, etc.

 

 

Keynote Lecture

Salvatore Distefano
Università degli Studi di Messina
Italy


Brief Bio
Salvatore Distefano is an Associate Professor at University of Messina, Italy, and Fellow Professor at Kazan Federal University, head of the Social and Urban Computing Group and of the Cisco Innovation Center in Kazan. He was formerly an Assistant Professor at Politecnico di Milano (2011-2015). In 2001 he got the master degree in Computer Engineering from University of Catania, and then, in 2006, he received the PhD degree on Computer Science and Engineering from University of Messina.
He authored and co-authored more than 170 scientific papers and contributions to international journals, conferences and books.
He took part to several national and international projects, such as Reservoir, Vision (EU FP7), SMSCOM (EU FP7 ERC Advanced Grant), Beacon, IoT-Open.EU (EU H2020).
He is a member of international conference committees and he is in the editorial boards of IEEE Transactions on Dependable and Secure Computing, International Journal of Performability Engineering, Journal of Cloud Computing, International Journal of Engineering and Industries, International Journal of Big Data, International Journal of Computer Science & Information Technology Applications, International Journal of Distributed Sensor Networks.
He has also acted as guest editors for special issues of the Journal of Risk and Reliability, Journal of Performability Engineering, ACM Performance Evaluation Review and IEEE Transactions on Dependable and Secure Computing
His main research interests include non-Markovian modelling; performance and reliability evaluation; dependability; Quality of Service/Experience; Service Level Agreement; Parallel and Distributed Computing, Grid, Cloud, Autonomic, Volunteer, Crowd, Edge, Fog Computing; Internet of Things; Smart Cities; Swarm and collective intelligence; Big Data; Software and Service Engineering. During his research activity, he contributed to the development of several tools such as WebSPN, ArgoPerformance, GS3 and Stack4Things.
He is also one of the co-founder of the SmartMe.io start up, a spin-off of the University of Messina established in 2017.


Abstract
Available soon.

 

 

The Future of Information Systems - Direct Execution of Enterprise Models, Almost Zero Programming

David Aveiro
University of Madeira / Madeira-ITI
Portugal


Brief Bio
David Aveiro is an Invited Assistant Professor at the Exact Sciences and Engineering Centre of the University of Madeira in Portugal. His research interests include organizational engineering and organizational change. His teaching interests include organizational engineering, database management systems and decision support systems. He holds a MSc and a PhD in Computer Science and Information Systems Engineering from Instituto Superior Técnico of the Technical University of Lisbon. His PhD theme was enterprise engineering and change and he applied the Design and Engineering Methodology (DEMO) to precisely specify the ontology of organizational change and control.


Abstract
Available soon.

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