Wogenstein, Florian; Gaul, Charly; Kropp, Peter; Scheidt, Jörg; Siebenhaar, Yannic; Drescher, Johannes (2018)
Wogenstein, Florian; Gaul, Charly; Kropp, Peter; Scheidt, Jörg; Siebenhaar, Yannic...
it - Information Technology 2018 60 (1), S. 11-19.
DOI: 10.1515/itit-2017-0016
In this paper we introduce the design and technical implementation of the citizen science project Migraine Radar. The goal of the project is to establish a large collection of migraine attack data in order to explore the trigger factors of migraine attacks. A main focus is the investigation of the influence of environmental factors like weather or changes in the geomagnetic activity on the frequency of migraine attacks. After registering with the project, participants report their migraine attack data using a web app or one of the smartphone apps implemented for Android and iOS. As a benefit, the system serves as a personal headache calendar and participants have access to statistics and individualized reports about their attacks. For scientific analysis the data are pre-processed and provided to the researchers in an anonymized way.
Wagener, Andreas (2018)
Wolff, Dietmar / Göbel, Richard (Hrsg.): Digitalisierung – Segen oder Fluch.
Wagener, Andreas (2018)
dpr – Digital Publishing Report, Nr. 1/2018, Trends 2018, S. 11.
Weber, Beatrix (2018)
Begleitforschung Smart Data: Big Data, Smart Data, Next?, S. 88-91.
Weber, Beatrix (2018)
Digitalisierung - Segen oder Fluch?, S. 101-123.
Wolff, Dietmar (2018)
Der Paritätische Sachsen-Anhalt, Blickpunkte 01/2018, S. 36-37.
Wolff, Dietmar; Göbel, Richard (2018)
Wolff, Dietmar (2018)
Digitaler Wandel in der Sozialwirtschaft - Grundlagen-Strategien-Praxis, S. 45-56.
Wolff, Dietmar (2018)
SOZIALwirtschaft 1/2018.
Plenk, Valentin; Lang, Sascha; Wogenstein, Florian (2017)
International Journal On Advances in Software 10 (3 und 4), 167-179.
This paper proposes to make complex production machines more user-friendly. Improved machines help the operator in case of an error message or a process event by displaying recommendations, such as “at the last 10 occurrences of this event the operators performed the following keystrokes”. The messages are generated from statistical data on former user- interaction and previous process-events. The data represents the knowledge of all the machine operators. The data is gathered by logging user-interaction and process-events during regular operation of the production machine. This approach allows to store the operators’ expert knowledge in the production machine without human intervention.
Wagener, Andreas (2017)
Marconomy, 12.12.17, https://www.marconomy.de/influencer-marketing-fuer-die-nische-a-670604/, 2017 2017.
Wagener, Andreas (2017)
Industry of Things, 11.12.17, https://www.industry-of-things.de/die-akzeptanz-von-smart-metern-als-voraussetzung-fuer-energie-40-a-666713/, 2017 2017.
Mit Carsten Kloth (2017): In: bizzenergy. Dezember 2017/Januar 2018, S. 50 – 55 (S. 55), 2017.
Wagener, Andreas (2017)
Bitcoin. Interview mit Mit: Gerhard Prockscha (2017): In: Radio Extra, 01.12.17.
Wagener, Andreas (2017)
BARC Congress für Business Intelligence und Datenmanagement 2017, 21.11.2017, Würzburg.
Wagener, Andreas (2017)
Digital Know-How Sessions der Commerzbank AG Frankfurt, 14.11.2017, Frankfurt a. M. .
Wagener, Andreas (2017)
Bietigheim-Bissinger Akademietage, 08.11.2017, Bietigheim-Bissingen.
Wagener, Andreas (2017)
Deutsche HR-Summit der F.A.Z.-Verlagsgruppe, 27.10.2017, Frankfurt a. M. .
Wagener, Andreas (2017)
Im Rahmen der (hochschulöffentlichen) Ringvorlesung „Digitalisierung, Industrie 4.0 & das Internet der Dinge“ an der Hochschule Hof, 18.10.2017, Hof.
Plenk, Valentin; Lang, Sascha; Wogenstein, Florian (2017)
Proceedings of CENTRIC 2017: The Tenth International Conference on Advances in Human-oriented and Personalized Mechanisms, Technologies, and Services, Athen.
We propose a system to make complex production machines more user-friendly by giving the operator recommendations, such as "in the last 10 occurrences of this event the operators performed the following keystrokes". We describe algorithms to generate the recommendations based on data on former user-interaction and process values and to store them in a knowledge base. We also propose algorithms to retrieve recommendations suited to the current process state. We evaluate their performance on simulated data and data gathered from real production machines.
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