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it - Information Technology

Methods and Applications of Informatics and Information Technology

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Volume 60, Issue 3


Efficient and fast monitoring and disruption management for a pressure diecast system

Yvonne Hegenbarth / Thomas Bartsch / Gerald H. Ristow
Published Online: 2018-06-28 | DOI: https://doi.org/10.1515/itit-2017-0039


An increasing amount of information is collected in industrial production processes. In many cases, this data is only accessible to the vendor of the machines involved in the production process. In the government-funded research project BigPro, we propose a flexible and fast Big Data platform that allows detection and reaction to incidents and anomalies in the production process in near real-time.

Keywords: Big Data; Streaming Analytics; Predictive Maintenance; Industry 4.0; Internet of Things; Incident; Reaction and Disruption Management

ACM CCS: Applied computingOperations researchIndustry and manufacturingCommand and control


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About the article

Yvonne Hegenbarth

Yvonne Hegenbarth successfully studied informatics (B.SC. 2016), in collaboration with the second largest German software vendor, Software AG, at the University of Applied Sciences in Darmstadt, Germany. At Software AG she is part of the research team and participated successfully in one international and one national project. Today she is continuing her studies (M.Sc.), focused on Data Science practices.

Thomas Bartsch

Thomas Bartsch is a State-Certified Engineer (since 2011). He is working in maintenance for more than 10 years. During his education to State-Certified Engineer he get some experience in automation technology in solar industry. Today he is the head of maintenance at Auto Heinen GmbH and responsible for a national research project.

Gerald H. Ristow

Gerald H. Ristow studied Physics at the University of Hannover, Germany, (Diplom 1988, Promotion 1991) and at the Georgia Institute of Technology, USA, (M.Sc. 1987). After various PostDoc positions, he joined the second largest German software vendor, Software AG, in 2001. There he worked as a consultant and many years in product development. Currently, he is part of the research team where he leads the Software AG activities in two international projects.

Received: 2017-12-21

Revised: 2018-05-03

Accepted: 2018-05-28

Published Online: 2018-06-28

Published in Print: 2018-07-01

Funding Source: Bundesministerium für Bildung und Forschung

Award identifier / Grant number: 01IS14011

This work was partly funded by the German Federal Ministry of Education and Research as part of the BigPro project (01IS14011).

Citation Information: it - Information Technology, Volume 60, Issue 3, Pages 165–171, ISSN (Online) 2196-7032, ISSN (Print) 1611-2776, DOI: https://doi.org/10.1515/itit-2017-0039.

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