From Representation to Mediation: Modeling Information Systems in a Digital World
Jan Recker, University of Hamburg, Germany
A Profile-aware Methodological Framework for Collaborative Multidimensional Modeling: Agro-biodiversity Case Study
Sandro Bimonte, INRAE, France
Querying Decentralized Knowledge Graphs
Hala Skaf-Molli, Nantes University, France
Database Systems and Information Management: Trends and a Vision
Volker Markl, German Research Center for Artificial Intelligence (DFKI) and Technische Universität Berlin (TU Berlin), Germany
From Representation to Mediation: Modeling Information Systems in a Digital World
Jan Recker
University of Hamburg
Germany
http://www.janrecker.com/
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Brief Bio
Prof Dr Jan Recker is a Professor for Information Systems and Digital Innovation in the Hamburg Business School at the University of Hamburg. He is also Adjunct Professor at the QUT Business School, Australia.
In his research he explores the intersection of technology, people and work. He works with particularly large organizations, such as Woolworths, SAP, Hilti, Commonwealth Bank, Lufthansa, Ubisoft, federal and state governments, and with particularly small organizations ("start-ups") in the consumer goods, information techology, and financial sectors. He tackles questions such as:
• How do small and large organizations deal with digital innovation and transformation?
• How do products and processes change through digitalization?
• How can digital solutions help building a sustainable future?
Jan's research in these areas draws on quantitative, qualitative and mixed field methods. His research has appeared in leading information systems, management science, software engineering, project management, computer science, and sociology journals. He has also written popular textbooks on scientific research and data analysis, which are in use in over 500 institutions in over 60 countries. He ranks as one of the most published information systems academics of all time. In 2019, he was named #1 business researcher under 40 years of age by the German Magazine Wirtschaftswoche. He was the youngest academic ever to be named an AIS fellow in 2018.
Abstract
The role of information systems is changing in an increasingly digitalized world. Does this situation mean that established conceptual modeling practices relevant to the analysis and design of systems must change as well? In this talk, I will answer this question with a definite and affirmative “yes”. I will review the traditional assumptions around the conceptual modeling of information systems and demonstrate how advances in digital technology increasingly challenge these assumptions. I will then present a new framework for conceptual modeling that is consistent with the emerging requirements of a digital world. The framework draws attention to the role of conceptual models as mediators between physical and digital realities. It identifies new research questions about grammars, methods, scripts, agents, and contexts that are situated in intertwined physical and digital realities. I will discuss several implications for conceptual modeling scholarship for systems analysis and design that relate to the necessity of developing new methods and grammars for conceptual modeling, broadening the methodological array of conceptual modeling scholarship, and considering new dependent variables.
A Profile-aware Methodological Framework for Collaborative Multidimensional Modeling: Agro-biodiversity Case Study
Sandro Bimonte
INRAE
France
Brief Bio
Sandro Bimonte is Reseach Director at French National Research Institute for Agriculture, Food and the Environment (France), and more exactly he is member of the TSCF laboratory. He received his PhD from INSA-Lyon, France (2004–2007). He is an editorial review board member of International Journal of Data Warehousing and Mining, International Journal of Decision Support System Technology, and international conferences such as ER, DOLAP, etc.He has published more than 100 papers in refereed journals and international conferences.His research activities concern spatial data warehouses and spatial OLAP, visual languages, geographic information systems, spatio-temporal databases, geovisualisation, Big Data, and IoT.He joined and coordinated several research projects (such as VGI4bio.fr and BEYOND) on the above areas.
Abstract
Multidimensional modeling, i.e., the design of cube schemata, has a key role in data warehouse (DW) projects, in self-service business intelligence, and in general to let users analyze data via the OLAP paradigm. Though an effective involvement of users in multidimensional modeling is crucial in these projects, not much has been said about how to establish a fruitful collaboration in projects involving numerous users with different skills, reputations, and degrees of authority. This issue is especially relevant in citizen science projects, where several volunteers can contribute their requirements despite not being formally-trained experts in the application domain. To fill this gap, we propose a framework for collaborative multidimensional modeling that can adapt itself to the pro-files and skills of the actors involved. We first classify users depending on their authoritativeness, skills, and engagement in the project. Then, following this classification, we identify four possible methodological scenarios and propose a profile-aware methodology supported by two sets of quality attributes. Finally, we describe a Group Decision Support System that implements our methodological framework and present some experiments carried out on a real case study.
Querying Decentralized Knowledge Graphs
Hala Skaf-Molli
Nantes University
France
www.loria.fr/~skaf
Brief Bio
Hala Skaf-Molli (ORCID, personal page, scholar) is an associate professor, HDR at the University of Nantes. She is a member of the GDD research team (LS2N Lab). Her research focuses on the Semantic Web, decentralized semantic data management, federated SPARQL queries, knowledge graphs, and Linked Data. Her last major publication on web preemption was presented in The Web Conference (WWW 2019). Currently, she is the coordinator of the national ANR project DeKaloG (Decentralized Knowledge Graph).
Abstract
Following the principles of linked data, billions of RDF data have been produced and hundreds of interconnected knowledge graphs are available through public SPARQL endpoints. However, existing SPARQL servers suffer from availability and scalability issues.
In this talk, I will present the latest research results that address these issues. I will present approaches that balance the cost of query processing between data providers and data consumers in order to reduce the pressure on data providers.
I will conclude my presentation with open research questions.
Database Systems and Information Management: Trends and a Vision
Volker Markl
German Research Center for Artificial Intelligence (DFKI) and Technische Universität Berlin (TU Berlin)
Germany
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Brief Bio
Volker Markl is a Full Professor and Chair of the Database Systems and Information Management (DIMA) Group at the Technische Universität Berlin (TU Berlin). At the German Research Center for Artificial Intelligence (DFKI), he is Chief Scientist and Head of the Intelligent Analytics for Massive Data Research Group. In addition, he is Director of the Berlin Institute for the Foundations of Learning and Data (BIFOLD), a merger of the Berlin Big Data Center (BBDC) and the Berlin Center for Machine Learning (BZML). BIFOLD is one of Germany's national Competence Centers for Artificial Intelligence and will further bolster ongoing collaborative research in scalable data management and Machine Learning. Dr. Markl is a database systems researcher conducting research at the intersection of distributed systems, scalable data processing, text mining, computer networks, machine learning, and applications in healthcare, logistics, Industry 4.0, and information marketplaces. Earlier in his career, he was a Research Staff Member and Project Leader at the IBM Almaden Research Center in San Jose, California, USA and a Research Group Leader at FORWISS, the Bavarian Research Center for Knowledge-based Systems located in Munich, Germany. Volker Markl is a computer science graduate from Technische Universität München, where he earned his Diploma in 1995 with a thesis on exception handling in programming languages. He earned his PhD in 1999 the area of multidimensional indexing under the supervision of Rudolf Bayer.
Volker Markl has published numerous scholarly papers on indexing, query optimization, lightweight information integration, and scalable data processing at prestigious venues. He holds 18 patents, has transferred technology into several commercial products, and has been involved in two successful startup exits. He has been both the Speaker and Principal Investigator for the Stratosphere Project, which resulted in a Humboldt Innovation Award as well as Apache Flink, the open-source big data analytics system. He currently serves as the President of the VLDB Endowment and was elected as one of Germany's leading Digital Minds (Digitale Köpfe) by the German Informatics (GI) Society. Volker also is a member of the Scientific Advisory Board of Software AG. Most recently, Volker and his team earned the ACM SIGMOD 2020 Best Paper Award, for their work on „ Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects “. In addition, Volker has been named as an ACM Fellow by the Association for Computing Machinery (ACM), the largest and oldest international association of computer scientists.
Abstract
The global database research community has greatly impacted the functionality and performance of data storage and processing systems along the dimensions that define “big data”, i.e., volume, velocity, variety, and veracity. Locally, over the past five years, we have also been working on varying fronts. Among our contributions are: (1) establishing a vision for a database-inspired big data analytics system, which unifies the best of database and distributed systems technologies, and augments it with concepts drawn from compilers (e.g., iterations) and data stream processing, as well as (2) forming a community of researchers and institutions to create the Stratosphere platform to realize our vision. One major result from these activities was Apache Flink, an open-source big data analytics platform and its thriving global community of developers and production users. Although much progress has been made, when looking at the overall big data stack, a major challenge for database research community still remains. That is, how to maintain the ease-of-use despite the increasing heterogeneity and complexity of data analytics, involving specialized engines for various aspects of an end-to-end data analytics pipeline, including, among others, graph-based, linear algebra-based, and relational-based algorithms, and the underlying, increasingly heterogeneous hardware and computing infrastructure. At TU Berlin, DFKI, and the Berlin Institute for Foundations of Learning and Data (BIFOLD) we currently aim to advance research in this field via the Nebula Stream and Agora projects. Our goal is to remedy some of the heterogeneity challenges that hamper developer productivity and limit the use of data science technologies to just the privileged few, who are coveted experts. In this talk, we will outline how state-of-the-art SPEs have to change to exploit the new capabilities of the IoT and showcase how we tackle IoT challenges in our own system, NebulaStream. We will also present our vision for Agora, an asset ecosystem that provides the technical infrastructure for offering and using data and algorithms, as well as physical infrastructure components.