Abstract
This paper describes a Big Data stream analytics platform developed within the DEWI project for processing upcoming events from wireless sensors installed in a truck. The platform consists of a Complex Event Processing (CEP) engine capable of triggering alarms from a predefined set of rules. In general these rules are characterized by multiple parameters, for which finding their optimal value usually yields a challenging task. In this paper we explain a methodology based on a meta-heuristic solver that is used as a wrapper to obtain optimal parametric rules for the CEP engine. In particular this approach optimizes CEP rules through the refinement of the parameters controlling their behavior based on an alarm detection improvement criterion. As a result the proposed scheme retrieves the rules parameterized in a detection-optimal fashion. Results for a certain use case – i.e. fuel level of the vehicle – are discussed towards assessing the performance gains provided by our method.
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Acknowledgments
The research from DEWI project (www.dewi-project.eu) [1] leading to these results has received funding from ARTEMIS Joint Undertaking under grant agreement no. 621353. The authors want to specially thank Parthasarathy Dhasarathy, from Volvo Technology AB, who helped in the use cases and the platform infrastructure definition.
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Olabarrieta, I.(., Torre-Bastida, A.I., Laña, I., Campos-Cordobes, S., Del Ser, J. (2017). A Heuristically Optimized Complex Event Processing Engine for Big Data Stream Analytics. In: Del Ser, J. (eds) Harmony Search Algorithm. ICHSA 2017. Advances in Intelligent Systems and Computing, vol 514. Springer, Singapore. https://doi.org/10.1007/978-981-10-3728-3_11
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DOI: https://doi.org/10.1007/978-981-10-3728-3_11
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