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Keynote Lectures

The Sensing Enterprise - Enterprise Information Systems in the Internet of Things
Sergio Gusmeroli, Engineering Ingegneria Informatica SPA, Italy

Making Process Mining Green - Using Event Data in a Responsible Way
Wil Van Der Aalst, Technische Universiteit Eindhoven, Netherlands

Towards Model-Driven Big-Data-as-a-Service
Ernesto Damiani, EBTIC-KUSTAR, United Arab Emirates

 

The Sensing Enterprise - Enterprise Information Systems in the Internet of Things

Sergio Gusmeroli
Engineering Ingegneria Informatica SPA
Italy
 

Brief Bio
Sergio Gusmeroli is a Senior Advisor for the Research and Innovation Unit in Engineering Ingegneria Informatica SPA (www.eng.it). As a researcher, Sergio has recently and is coordinating large scale FP7 projects and H2020 R&I actions (e.g. Elliot MSEE FITMAN OSMOSE PSYMBIOSYS) in the field of IoT technologies especially applied to Manufacturing Industries. As an innovation manager, Sergio has been co-conducting with IDC the EC-commissioned study Definition of a research and innovation policy leveraging Cloud Computing and IoT Combination, SMART 2013/0037 and is coordinating a H2020 Innovation Action about full adoption of IoT-enabled Cyber Physical Production Systems in Industry.


Abstract
The keynote aims at describing how recent IT innovations in the field of IoT (e.g. cyber physical systems, smart networks, edge computing, smart objects, business intelligence, data analytics) are influencing the evolution of Enterprise Information Systems. Thanks to the advent of IOT, Enterprise PLM systems are abandoning the walled garden of Design and Engineering, while embracing the whole product lifecycle, including post-sales services and addressing circular economy challenges, becoming this way “Things Lifecycle Management Systems”. At the same time, MES (Manufacturing Execution Systems) need to consider Industry 4.0 evolution in production systems and the advent of Cyber Physical and Systems. What it is not fully clear up to now is the IOT-driven evolution of ERP and SCM systems and how decision making at the level of configuration, planning and scheduling of enterprises’ resources could be implemented by distributed edge-computing architectures. We call this new concept “The Sensing Proactive Enterprise”. The speech is inspired by several EC-funded R&I projects in the field of “IOT for Enterprise” under the FP7 and H2020 Framework Programmes in the Net Innovation unit E3 of the Future Internet (DG CNECT).



 

 

Making Process Mining Green - Using Event Data in a Responsible Way

Wil Van Der Aalst
Technische Universiteit Eindhoven
Netherlands
 

Brief Bio
Prof.dr.ir. Wil van der Aalst is a full professor of Information Systems at the Technische Universiteit Eindhoven (TU/e). At TU/e he is the scientific director of the Data Science Center Eindhoven (DSC/e). Since 2003 he holds a part-time position at Queensland University of Technology (QUT). His personal research interests include workflow management, process mining, Petri nets, business process management, process modeling, and process analysis. Wil van der Aalst has published more than 180 journal papers, 18 books (as author or editor), 400 refereed conference/workshop publications, and 60 book chapters. Many of his papers are highly cited (he one of the most cited computer scientists in the world and has an H-index of 122 according to Google Scholar) and his ideas have influenced researchers, software developers, and standardization committees working on process support. He has been a co-chair of many conferences including the Business Process Management conference, the International Conference on Cooperative Information Systems, the International conference on the Application and Theory of Petri Nets, and the IEEE International Conference on Services Computing. He is also editor/member of the editorial board of several journals, including Computing, Distributed and Parallel Databases, Software and Systems Modeling, the International Journal of Business Process Integration and Management, the International Journal on Enterprise Modelling and Information Systems Architectures, Computers in Industry, Business & Information Systems Engineering, IEEE Transactions on Services Computing, Lecture Notes in Business Information Processing, and Transactions on Petri Nets and Other Models of Concurrency. In 2012, he received the degree of doctor honoris causa from Hasselt University in Belgium. He served as scientific director of the International Laboratory of Process-Aware Information Systems of the National Research University, Higher School of Economics in Moscow. In 2013, he was appointed as Distinguished University Professor of TU/e and was awarded an honorary guest professorship at Tsinghua University. In 2015, he was appointed as honorary professor at the National Research University, Higher School of Economics in Moscow. He is also a member of the Royal Netherlands Academy of Arts and Sciences (Koninklijke Nederlandse Akademie van Wetenschappen), Royal Holland Society of Sciences and Humanities (Koninklijke Hollandsche Maatschappij der Wetenschappen) and the Academy of Europe (Academia Europaea).


Abstract
Process mining provides new ways to utilize the abundance of event data in world surrounding us. These event data enable new forms of analysis facilitating process improvement. Process mining provides a novel set of tools to discover the real process, to detect deviations from some normative process, and to analyze bottlenecks and waste. Process mining will be an integral part of the data scientist's toolbox. Process mining is as generic as a spreadsheet. Where spreadsheets work with numbers, process mining starts from event data with the aim to analyze processes. Events (often hidden in Big Data) can be considered as the "new oil" and process mining aims to transform these into new forms of "energy": insights, diagnostics, models, predictions, and automated decisions. However, the process of transforming "new oil" (event data) into "new energy" (analytics) may negatively impact citizens, patients, customers, and employees. Systematic discrimination based on data, invasions of privacy, non-transparent life-changing decisions, and inaccurate conclusions illustrate that data science techniques may lead to new forms of "pollution". We use the term ``Green Data Science'' for technological solutions that enable individuals, organizations and society to reap the benefits from the widespread availability of data while ensuring fairness, confidentiality, accuracy, and transparency. To illustrate the scientific challenges related to "Green Data Science'', we focus on "Green Process Mining" as a concrete example. After introducing process mining, Wil van der Aalst will try to answer the question: How to benefit from process mining while avoiding "pollutions" related to unfairness, undesired disclosures, inaccuracies, and non-transparency?



 

 

Towards Model-Driven Big-Data-as-a-Service

Ernesto Damiani
EBTIC-KUSTAR
United Arab Emirates
 

Brief Bio
Ernesto Damiani is the Director of the Information Security Research center  of Khalifa University, Abu Dhabi where he also leads the Big Data Initiativce at the Etisalat British Telecom innovation Center. He is on extended leave from the Department of Computer Science, Università degli Studi di Milano, Italy, where he leads the SESAR research lab. Ernesto's research interests include secure service-oriented architectures, privacy-preserving Big Data analytics and Cyber-Physical Systems security. Ernesto holds/has held visiting positions at a number of international institutions, including George Mason University in Virginia, US, Tokyo Denki University, Japan, LaTrobe University in Melbourne, Australia, and the Institut National des Sciences Appliquées (INSA) at Lyon, France. He is a Fellow of the Japanese Society for the Progress of Science. He has been Principal Investigator in a number of large-scale research projects funded by the European Commission, the Italian Ministry of Research and by private companies such as British Telecom, Cisco Systems, SAP, Telecom Italia, Siemens Networks (now Nokia Siemens) and many others.  Ernesto serves in the editorial board of several international journals; among others, he is the EIC of the International Journal on Big Data and of the International Journal of Knowledge and Learning. He is Associate Editor of IEEE Transactions on Service-oriented Computing and of the IEEE Transactions on Fuzzy Systems. Ernesto is a Senior Member of the IEEE and served as Vice-Chair of the IEEE Technical Committee on Industrial Informatics. In 2008, Ernesto was nominated ACM Distinguished Scientist and received the Chester Sall Award from the IEEE Industrial Electronics Society. Ernesto has co-authored over 350 scientific papers and many books, including "Open Source Systems Security Certification" (Springer 2009).


Abstract
Much work has been done to show that model-driven development—following the classic Model-Driven Architecture (MDA) approach—is also advantageous in the context of data intensive applications, which have become the norm in key business domains like CRM, sales/marketing, HRM and Business Process analysis. As a result, a number of methodologies have been proposed for developing data-intensive applications via a chain of model transformations. Recently, however, the advent of Big Data has brought on a new fundamental issue. While model transformations usually introduce each architecture dependent feature at a fixed point of the model refinement chain, Big Data computations can only fulfil their promises of scalability and efficiency if binding architectural and data modelling decisions are taken at the last possible moment, i.e. when information on the data distribution, volume and nature becomes available.
In this talk, we argue that Big-Data-as-a-Service, where data features can be different for each deployment of the data pipeline, can benefit from a Software-Product-Line (SPL) parametric  approach to keep multiple alternative models alive and postpone binding modelling decision in order to make at at the “right” (i.e., the last possible) moment. We compare traditional model transformations with our “lazy” ones and discuss how to delay and parametrize modelling decisions for all key aspects of a Big Data application, including data modelling, parallelization and visualization.




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