Service-Oriented Cognitive Analytics for Smart Service Systems: A Research Agenda Hirt, Robin Kühl, Niklas Schmitz, Björn Satzger, Gerhard 2017-12-28T00:51:24Z 2017-12-28T00:51:24Z 2018-01-03
dc.description.abstract The development of analytical solutions for smart services systems relies on data. Typically, this data is distributed across various entities of the system. Cognitive learning allows to find patterns and to make predictions across these distributed data sources, yet its potential is not fully explored. Challenges that impede a cross-entity data analysis concern organizational challenges (e.g., confidentiality), algorithmic challenges (e.g., robustness) as well as technical challenges (e.g., data processing). So far, there is no comprehensive approach to build cognitive analytics solutions, if data is distributed across different entities of a smart service system. This work proposes a research agenda for the development of a service-oriented cognitive analytics framework. The analytics framework uses a centralized cognitive aggregation model to combine predictions being made by each entity of the service system. Based on this research agenda, we plan to develop and evaluate the cognitive analytics framework in future research.
dc.format.extent 8 pages
dc.identifier.doi 10.24251/HICSS.2018.203
dc.identifier.isbn 978-0-9981331-1-9
dc.language.iso eng
dc.relation.ispartof Proceedings of the 51st Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Smart Service Systems: Analytic, Cognition and Innovation
dc.subject analytics framework, cross-entity learning, cognitive learning, research agenda, smart service systems
dc.title Service-Oriented Cognitive Analytics for Smart Service Systems: A Research Agenda
dc.type Conference Paper
dc.type.dcmi Text
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