Gerti Kappel

O.Univ.Prof.in Dipl.-Ing.in
Mag.a Dr.in techn.

Gerti Kappel

Email: gertrude.kappel@tuwien.ac.at
Phone: +43-1-58801-18870
Office: FB0109 (1040 Wien, Erzherzog-Johann-Platz 1)

About:
Gerti Kappel is full professor at the Institute of Information Systems Engineering at TU Wien, in the Business Informatics Group. Prior to that, from 1993 to 2001, she was a full professor of computer science (database systems) and head of the Department of Information Systems at the Johannes Kepler University Linz.
From 2016 to 2019, she was a member of the dean’s team of the Faculty of Informatics responsible for research, diversity, and financial affairs. Since the beginning of 2020 she acts as the dean of the Faculty of Informatics at TU Wien.
Her current research interests include Model Engineering, Web Engineering, and Process Engineering, with a special emphasis on cyber-physical production systems. Striving for the unity of research and teaching, she co-authored and co-edited among others „UML@Work“ (dpunkt.verlag, 3rd ed, 2005), „UML@Classroom“ (Springer, 2015), and „Web Engineering“ (Wiley, 2006).
Orcid: 0000-0002-4758-9436
Keywords: Process Engineering, Data Engineering, Services Engineering, UML and XML, Business Process Management (BPM), Model Engineering, Workflow Management Systems (WFMS), Web Engineering, Object Orientation, Software Engineering
Roles: Head of Services, Full Professor

Publications

Data Analytics for Industrial Process Improvement A Vision Paper

Stefan Thalmann

Juergen Mangler

Tobias Schreck

Christian Huemer

Marc Streit

Florian Pauker

Georg Weichhart

Stefan Schulte

Christian Kittl

Christoph Pollak

Matej Vukovic

Gerti Kappel

Milot Gashi

Stefanie Rinderle-Ma

Josef Suschnigg

Nikolina Jekic

Stefanie Lindstaedt

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Handle: 20.500.12708/57688; DOI: 10.1109/cbi.2018.10051; Year: 2018; Issued On: 2018-01-01; Type: Publication; Subtype: Inproceedings; Peer Reviewed:

Keywords:
Astract: Nowadays, manufacturers are increasingly able to collect and analyze data from sensors on manufacturing equipment, and also from other types of machinery, such as smart meters, pipelines, delivery trucks, etc. Nevertheless, many manufacturers are not yet ready to use analytics beyond a tool to track historical performance. However, just knowing what happened and why it happened does not use the full potential of the data and is not sufficient anymore. Manufacturers need to know what happens next and what actions to take in order to get optimal results. It is a challenge to develop advanced analytics techniques including machine learning and predictive algorithms to transform data into relevant information for gaining useful insights to take appropriate action. In the proposed research we target new analytic methods and tools that make insights not only more understandable but also actionable by decision makers. The latter requires that the results of data analytics have an immediate effect on the processes of the manufacturer. Thereby, data analytics has the potential to improve industrial processes by reducing maintenance costs, avoiding equipment failures and improving business operations. Accordingly, the overall objective of this project is to develop a set of tools - including algorithms, analytic machinery, planning approaches and visualizations - for industrial process improvements based on data analytics.


  Thalmann, S., Mangler, J., Schreck, T., Huemer, C., Streit, M., Pauker, F., Weichhart, G., Schulte, S., Kittl, C., Pollak, C., Vukovic, M., Kappel, G., Gashi, M., Rinderle-Ma, S., Suschnigg, J., Jekic, N., & Lindstaedt, S. (2018). Data Analytics for Industrial Process Improvement A Vision Paper. In 2018 IEEE 20th Conference on Business Informatics (CBI). 2018 IEEE 20th Conference on Business Informatics (CBI), Vienna, Austria. IEEE Computer Society. https://doi.org/10.1109/cbi.2018.10051

Studieneinstieg, nicht -ausstieg!

Elisabeth Wetzinger

Gerti Kappel

Gerald Futschek

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Handle: 20.500.12708/146946; Year: 2017; Issued On: 2017-01-01; Type: Publication; Subtype: Article;

Keywords:
Astract: At the faculty for Informatics, TU Wien, there is a great heterogenity among students regarding their educational requirements, especially concerning prior knowledge of programming. Nevertheless, programming is an essential part of the first year curricula of CS and Business Informatics bachelor studies and it constitutes a base, a tool or content of further courses throughout the further study progress. To support prospective students in programming already prior to their start of studies, a MOOC (massive open online course) on introductory programming has been developed at the Faculty of Informatics at TU Wien. During summer 2017 it has been successfully implemented with a limited participants group of 579 prospective students and a small group of other internal and external test persons. Ongoing evaluations have already shown that the course and its implementation as a MOOC has the significant potential to serve as an additional bridging-course offer to prospective MINT students.


  Wetzinger, E., Kappel, G., & Futschek, G. (2017). Studieneinstieg, nicht -ausstieg! TU Freihaus, 44. http://hdl.handle.net/20.500.12708/146946

Model-Driven Systems Engineering: Principles and Application in the CPPS Domain

Luca Berardinelli

Alexandra Mazak

Oliver Alt

Manuel Wimmer

Gerti Kappel

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Handle: 20.500.12708/29204; DOI: 10.1007/978-3-319-56345-9_11; Year: 2017; Issued On: 2017-01-01; Type: Publication; Subtype: Book Contribution; Peer Reviewed:

Keywords:


  Berardinelli, L., Mazak, A., Alt, O., Wimmer, M., & Kappel, G. (2017). Model-Driven Systems Engineering: Principles and Application in the CPPS Domain. In Multi-Disciplinary Engineering for Cyber-Physical Production Systems (pp. 261–299). Springer. https://doi.org/10.1007/978-3-319-56345-9_11

Rahmenwerk zur modellbasierten horizontalen und vertikalen Integration von Standards für Industrie 4.0

Alexandra Mazak

Wolfgang Kastner

Handle: 20.500.12708/29028; DOI: 10.1007/978-3-662-45537-1_94-1; Year: 2016; Issued On: 2016-01-01; Type: Publication; Subtype: Book Contribution; Peer Reviewed:

Keywords:
Astract: In Anlehnung an die Umsetzungsempfehlung des deutschen Arbeitskreises zu Industrie 4.0 widmen wir uns in diesem Kapitel dem offenen Handlungsfeld der Standardisierung und Referenzarchitektur im Kontext einer modellbasierten horizontalen und vertikalen Integration. Wir zeigen, dass die Zusammenführung international etablierter Standards genutzt werden kann, um eine flexible Informationsarchitektur zu schaffen. Zu diesem Zweck präsentieren wir ein offenes, dreidimensionales Rahmenwerk von Standards für Industrie 4.0. Die erste Dimension berücksichtigt die Unterscheidung zwischen den unterschiedlichen Ebenen in einem Unternehmen, in Anlehnung an die klassische Automatisierungspyramide. Die zweite Dimension unterscheidet zwischen den internen und den externen Aspekten der horizontalen und vertikalen Integration. Die dritte Dimension differenziert zwischen der wirtschaftlichen Tätigkeit der teilneh- menden Unternehmen im Wertschöpfungsnetzwerk und den technischen Aspekten des nahtlosen Daten- und Informationsaustausches.


  Mazak, A., Wimmer, M., Huemer, C., Kappel, G., & Kastner, W. (2016). Rahmenwerk zur modellbasierten horizontalen und vertikalen Integration von Standards für Industrie 4.0. In B. Vogel-Heuser (Ed.), Handbuch Industrie 4.0 (pp. 1–22). Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-662-45537-1_94-1

From Architecture Modeling to Application Provisioning for the Cloud by Combining UML and TOSCA

Alexander Bergmayr

Uwe Breitenbücher

Oliver Kopp

Manuel Wimmer

Gerti Kappel

Frank Leymann

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Handle: 20.500.12708/56450; DOI: 10.5220/0005806900970108; Year: 2016; Issued On: 2016-01-01; Type: Publication; Subtype: Inproceedings; Peer Reviewed:

Keywords:


  Bergmayr, A., Breitenbücher, U., Kopp, O., Wimmer, M., Kappel, G., & Leymann, F. (2016). From Architecture Modeling to Application Provisioning for the Cloud by Combining UML and TOSCA. In Proceedings of the 6th International Conference on Cloud Computing and Services Science. 6th International Conference on Cloud Computing and Services Science (CLOSER), Rom, Italy. https://doi.org/10.5220/0005806900970108