Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2019

Kaiser Rene, Thalmann Stefan, Pammer-Schindler Viktoria, Fessl Angela

Collaborating in a Research and Development Project: Knowledge Protection Practices applied in a Co-opetitive Setting

10th Conference Professional Knowledge Management, Data-Driven Knowledge Management workshop, proWM’19, Potsdam, DE, 2019

Konferenz
Organisations participate in collaborative projects that include competitors for a number of strategic reasons, even whilst knowing that this requires them to consider both knowledge sharing and knowledge protection throughout collaboration. In this paper, we investigated which knowledge protection practices representatives of organizations employ in a collaborative research and innovation project that can be characterized as a co-opetitive setting. We conducted a series of 30 interviews and report the following seven practices in structured form: restrictive partner selection in operative project tasks, communication through a gatekeeper, to limit access to a central platform, to hide details of machine data dumps, to have data not leave a factory for analysis, a generic model enabling to hide usage parameters, and to apply legal measures. When connecting each practice to a priori literature, we find three practices focussing on collaborative data analytics tasks had not yet been covered so far.
2019

Thalmann Stefan, Gursch Heimo, Suschnigg Josef, Gashi Milot, Ennsbrunner Helmut, Fuchs Anna Katharina, Schreck Tobias, Mutlu Belgin, Mangler Jürgen, Huemer Christian, Lindstaedt Stefanie

Cognitive Decision Support for Industrial Product Life Cycles: A Position Paper

Proceedings of the Eleventh International Conference on Advanced Cognitive Technologies and Applications (COGNITIVE 2019), Marta Franova, Charlotte Sennersten, Jayfus T. Doswell, IARIA, Venice, Italy, 2019

Konferenz
Current trends in manufacturing lead to more intelligent products, produced in global supply chains in shorter cycles, taking more and complex requirements into account. To manage this increasing complexity, cognitive decision support systems, building on data analytic approaches and focusing on the product life cycle, stages seem a promising approach. With two high-tech companies (world market leader in their domains) from Austria, we are approaching this challenge and jointly develop cognitive decision support systems for three real world industrial use cases. Within this position paper, we introduce our understanding of cognitive decision support and we introduce three industrial use cases, focusing on the requirements for cognitive decision support. Finally, we describe our preliminary solution approach for each use case and our next steps.
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