Enterprises as Model-Driven Systems
Henderik A. Proper, Luxembourg Institute of Science and Technology, Luxembourg
Compressing Big OLAP Data Cubes in Big Data Analytics Systems: New Paradigms and Future Research Perspectives
Alfredo Cuzzocrea, University of Calabria, Italy
Mining Knowledge Graphs From Loosely Structured Processes: A Use Case From Emailing Systems
Walid Gaaloul, Télécom SudParis, France
Enterprises as Model-Driven Systems
Henderik A. Proper
Luxembourg Institute of Science and Technology
Luxembourg
http://www.erikproper.eu
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Brief Bio
Prof.dr. Henderik A. Proper, Erik for friends, is an FNR PEARL Laureate, and is a senior research manager within the Computer Science (ITIS) department of the Luxembourg Institute of Science and Technology (LIST). He is also Adjunct Professor in Data & Knowledge Engineering at the University of Luxembourg. He regularly provides guest lectures within different MSc programmes offered by the University of Luxembourg (LU), the University of Lorraine (FR), TU Wien (AT), the University of Namur (BE), Antwerp University (BE), and TIAS (NL). Erik has a mixed background, covering a variety of roles in both academia and industry. His core research drive is the development of theories that work. In other words, Erik focuses on research that leads to results that have both theoretical rigour and practical relevance. His general research interest concerns the foundations and applications of domain modelling. Over the past 20 years, he has applied this research drive and general research interest towards the further development of the field of enterprise engineering, and enterprise modelling in particular. His long experience in teaching and coaching a wide variety of people enables him to involve and engage others in this development. He has co-authored several journal papers, conference publications and books. Erik received his Master's degree from the University of Nijmegen, The Netherlands in May 1990, and received his PhD (with distinction) from the same University in April 1994. In his Doctoral thesis he developed a theory for conceptual modelling of evolving application domains, yielding a formal specification of evolving information systems. After receiving his PhD, Erik became a senior research fellow at the Computer Science Department of the University of Queensland, Brisbane, Australia. During that period he also conducted research in the Asymetrix Research Lab at that University for Asymetrix Corp, Seattle, Washington. In 1995 he became a lecturer at the School of Information Systems from the Queensland University of Technology, Brisbane, Australia. During this period he was also seconded as a senior researcher to the Distributed Systems Technology Centre (DSTC), a Cooperative Research Centre funded by the Australian government. From 1997 to 2001, Erik worked in industry. First as a consultant at Origin, Amsterdam, The Netherlands, and later as a research consultant and principal scientist at the Ordina Institute for Research and Innovation, Gouda, The Netherlands. In June 2001, Erik returned to academia, where he became an adjunct Professor at the Radboud University Nijmegen. In September 2002, Erik obtained a full-time Professorship position at the Radboud University Nijmegen. In January of 2008, he went back to combining industry and academia, by combining his Professorship with consulting and innovation at Capgemini, with the aim of more tightly combining his theoretical and practical work. Finally, in May 2010 Erik moved to the Luxembourg Institute of Science and Technology as a PEARL chair, while initially also continuing his chair at the Radboud University Nijmegen in the Netherlands. As of June 2017, Erik also holds a chair in Data & Knowledge Engineering at the University of Luxembourg. As of January 2022, Erik is vice-chair of the IFIP 8.1 working group, while also being the representative for the Netherlands in IFIP's TC8 technical committee. He is also the Stellvertretender Sprecher (vice chair) of the EMISA working group of the German Computer Science Society (Gesellschaft für Informatik), as well as a member of the management team of the Enterprise Engineering Network.
Abstract
In this keynote, we will look at enterprises (companies, organizations, agencies, factories, ...) from the perspective of them being essentially model-driven systems. We will start with a short reflection on what a model is, where it is important to realize that the notion of model involves more than "boxes and lines" diagrams.
From this broader perspective, we then explore the natural role that models play in the (continuous) development, operation, and regulation of enterprises. New technologies, such as AI, low-code, rule engines, IoT, Digital Twins, etc, provide enablers for the usage of models (in the broader sense) in enterprises, while some of these technologies (e.g. low-code, rule engines, big data, and explainable AI) actually put even more stress on the role of models. We therefore finish this keynote with a discussion on some of the resulting opportunities and challenges.
Compressing Big OLAP Data Cubes in Big Data Analytics Systems: New Paradigms and Future Research Perspectives
Alfredo Cuzzocrea
University of Calabria
Italy
Brief Bio
Alfredo Cuzzocrea is Professor of Computer Engineering at the University of Calabria, Rende, Italy. He also covers the role of Full Professor in Computer Engineering at the University of Lorraine, Nancy, France, where he holds the Excellence Chair in Computer Engineering. He is Head of the Big Data Engineering and Analytics Lab of the University of Calabria, Italy. He is also Research Fellow of the National Research Council (CNR), Italy. Previously, he has been International Senior Research Fellow of the ISITE-BFC Research Excellence Program of the Ministry of Higher Education and Research (MESR), France. He holds several visiting professor positions worldwide. His current research interests span the following scientific fields: big data, database systems, data mining, data warehousing, and knowledge discovery. His research results have been awarded largely.
Abstract
In the current big data era, big data analytics systems play a leading role due to their popularity in a wide collection of application scenarios, ranging from healthcare systems to e-science platforms, from social networks to smart cities, from intelligent transportation systems to graph analysis tools, and so forth. Among various proposals, multidimensional big data analytics methodologies, which basically are based on well-understood OLAP paradigms, are gaining the momentum due to their flexibility and expressiveness power. In this so-delineated scenario, performance issues clearly represent a significant obstacle to the effective impact of these methodologies in real-life settings. Data compression techniques indeed represent successful approaches to face-off performance drawbacks in big multidimensional data analytics systems. According to this main concept, the idea of compressing big OLAP data cubes is a natural proposal arising in this field. Inspired by this main framework, in this talk we focus the attention on the issue of effectively and efficiently supporting big OLAP data cube compression in modern big data analytics systems. In particular, we first report on some recent proposals in this field, and then introduce some effective and efficient approaches we proposed in the context of the investigated research area, tailored to well-known big data requirements. Future research perspectives are presented and discussed as well.
Mining Knowledge Graphs From Loosely Structured Processes: A Use Case From Emailing Systems
Walid Gaaloul
Télécom SudParis
France
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Brief Bio
Prof.dr.ir. Walid Gaaloul is a professor at Télécom SudParis an engineering school (grande école d'ingénieurs) in the field of Information and Communication Technology. Télécom SudParis is part of the Institut Mines Télécom and Institut Polytechnique de Paris University. Walid Gaaloul is member of the Computer Science Department of Télécom SudParis. He is the deputy director of the research laboratory SAMOVAR and the leader of ACMES, a research team of SAMOVAR laboratory. Before joining Télécom SudParis, he was a researcher at the Digital Enterprise Research Institute (DERI) and an adjunct lecturer in the National University of Ireland, Galway (NUIG). He holds an M.S. (2002) and a Ph.D. (2006) in computer science from the University of Lorraine, France, and a habilitation (2014) from Pierre et Marie Curie University, Paris, France. He was a junior researcher in the Lorraine Laboratory of IT Research and its Applications (LORIA-INRIA) and a teaching assistant in the University of Lorraine, France. His research interests are on Process Mining, Business Process Management, Cloud Computing, Service Oriented Computing. Walid Gaaloul has published over 200 research papers in these domains. He serves as program committee member and reviewer at many international journals and conferences and has been participating in several national and European research projects.
Abstract
Process-oriented data analysis techniques allow organizations to understand how their processes operate, where modifications are needed and where enhancements are possible. These techniques assume that process data have a structured format. This implicitly means that the underlying processes are structured and therefore are totally executed in the information system. However, in several domains, processes are executed outside the information system using informal methods such as communication tools (e.g. email exchange, IM, etc.). Managing complaints from customers, resolving an insurance claim and internal audits are all examples of processes that require ad-hoc day-to-day tasks (e.g., send an email, schedule meetings, record notes, gather feedback, etc.). These processes are unstructured in nature meaning they have a start, but the activities are not necessarily consistent. Furthermore, these processes often involve a lot of people and communication.
Analyzing unstructured processes is challenging for two reasons. First, because of the unstructured nature, the corresponding data exhibit a high variability, which can only be explained when associated to a context. This latter is often unpredictable and therefore, it is difficult to store data according to a predefined schema. Second, data of unstructured processes often reside in unstructured sources such as emails. To enable process analysis, automated techniques need to be developed to extract process related knowledge. To address the first limitation, we propose to store process data in knowledge graphs. In particular, we use labeled property graphs, which incorporate RDF as well as any other type of data. Second, we propose automated techniques to construct knowledge graphs by extracting process related data from natural language texts. As a use case, we use emailing systems.