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
Software Defined Cities
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
cs.gmu.edu/~brodsky
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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 16 PhD students and currently advises other seven. 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. 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.
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
http://www.lancs.ac.uk/staff/angelov
Brief Bio
Prof. Angelov (MEng 1989, PhD 1993, DSc 2015) is a Fellow of the IEEE, of the IET and of the HEA. He is Vice President of the International Neural Networks Society (INNS) for Conference and Governor of the Systems, Man and Cybernetics Society of the IEEE. He has 25+ years of professional experience in high level research and holds a Personal Chair in Intelligent Systems at Lancaster University, UK. He leads the Data Science group at the School of Computing and Communications which includes over 20 academics, researchers and PhD students. He has authored or co-authored 300 peer-reviewed publications in leading journals, peer-reviewed conference proceedings, 6 patents, two research monographs (by Wiley, 2012 and Springer, 2002) cited over 6660+ times with an h-index of 39 and i10-index of 112. His single most cited paper has 820 citations. He has an active research portfolio in the area of computational intelligence and machine learning and internationally recognised results into online and evolving learning and algorithms for knowledge extraction in the form of human-intelligible fuzzy rule-based systems. Prof. Angelov leads numerous projects (including several multimillion ones) funded by UK research councils, EU, industry, UK MoD. His research was recognised by ‘The Engineer Innovation and Technology 2008 Special Award’ and ‘For outstanding Services’ (2013) by IEEE and INNS. He is also the founding co-Editor-in-Chief of Springer’s journal on Evolving Systems and Associate Editor of several leading international scientific journals, including IEEE Transactions on Fuzzy Systems (the IEEE Transactions with the highest impact factor) of the IEEE Transactions on Systems, Man and Cybernetics as well as of several other journals such as Applied Soft Computing, Fuzzy Sets and Systems, Soft Computing, etc. He gave over a dozen plenary and key note talks at high profile conferences. Prof. Angelov was General co-Chair of a number of high profile conferences including IJCNN2013, Dallas, TX; IJCNN2015, Killarney, Ireland; the inaugural INNS Conference on Big Data, San Francisco; the 2nd INNS Conference on Big Data, Thessaloniki, Greece and a series of annual IEEE Symposia on Evolving and Adaptive Intelligent Systems. Dr Angelov is the founding Chair of the Technical Committee on Evolving Intelligent Systems, SMC Society of the IEEE and was previously chairing the Standards Committee of the Computational Intelligent Society of the IEEE (2010-2012). He was also a member of International Program Committee of over 100 international conferences (primarily IEEE). More details 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.
Software Defined Cities
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 ComputingHis 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
A Smart City represents an improvement of today cities that strategically exploits many smart factors to increase the city sustainable growth and strengthen city functions, while ensuring citizen quality of life and health.
Cities can be perceived as ecosystems of "things" which citizens daily interact with: street furniture, public buildings, transportation, monuments, public lighting as well as personal smartphones.
Thanks to recent advances in ICT such things can be considered always interconnected also providing sensing and actuating facilities according to Internet of Things and Cyber Physical Systems models.
Creating smart services that exploit such a complex infrastructure is a fundamental and current challenge, as well as making the Smart City ecosystem sustainable.
To this extent, a solution could be on adopting the Software Defined Cities (SDC) approach, which is based on an IoT-Cloud infrastructure that, starting from the well known concept of Software Defined paradigms, is able to transform this complex ecosystem into a simple and "programmable" environment where municipalities, companies, scientists, and citizens can easily collaborate in developing innovative smart services.
This way, by enabling reuse and sharing of resources and services through programmability, a new wave of sustainable Smart Cities could be triggered by the SDC, built up from existing infrastructure and facilities thus reducing budgets and saving costs.
In this talk the main concepts and ideas behind the SDC approach will be discussed.
The adoption of the SDC approach on a real case study, the #SmartME (http://smartme.unime.it/) project, a low-cost crowdfunding initiative aiming at turning Messina smart, will be provided to show its feasibility and effectiveness.
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
Automatic code generation from models and software as a service platforms are recent and promising trends in information systems and software engineering areas to reduce development effort and increase time to market. We also see initiatives in platforms offering Business Processes as a Service (BPaas). However, these initiatives still fall short in solving the still major problem of most IT initiatives failing to fully satisfy business users and needs, normally due to lack of full comprehension of all needed aspects of the enterprise. The Enterprise Engineering discipline with its principles and theories has been advancing in the last years in achieving a more comprehensive, coherent, concise and complete view of the most relevant aspects of an enterprise. With this background, we are developing a conceptual framework and associated prototype to allow the direct execution of enterprise models which specify both the process and information views in a coherent and integrated way, while allowing the specification of business rules in a distributed and modular way, with the aim to reduce the need of programming and, consequently, code generation to almost zero. In this talk we will explore the main contributions brought by enterprise engineering and the conceptual framework and prototype being developed, towards or vision and aim of enterprise model execution as a service (EMEaas).