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On the Use of Provalets in a Predictive Maintenance Use Case

: Paschke, A.

Volltext ()

Athan, T.:
RuleML-SUP 2016. Supplementary Proceedings. Online resource : Proceedings of the RuleML 2016 Challenge, Doctoral Consortium and Industry Track hosted by the 10th International Web Rule Symposium (RuleML 2016). New York, USA, July 6-9, 2016
New York/NY, 2016 (CEUR Workshop Proceedings 1620)
14 S.
International Web Rule Symposium (RuleML) <10, 2016, New York/NY>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer FOKUS ()

In this paper we report on a predictive maintenance use cases using Provalet rule agents for implementing expressive rule-based streaming analytics and decision logic on top of online machine learning prediction models, which are dynamically applied to the streaming data coming from on-board asset monitoring sensors. Provalets are component-based mobile agents for rule-based inference analytics, which can be dynamically deployed as microservices into container environments via simple REST calls.