A Heuristically Optimized Complex Event Processing Engine for Big Data Stream Analytics
| Autor: | Javier Del Ser, Ibai Laña, Sergio Campos-Cordobés, Ana I. Torre-Bastida, Ignacio Olabarrieta |
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| Rok vydání: | 2017 |
| Předmět: |
010302 applied physics
Scheme (programming language) Big Data Optimization Computer science business.industry Big data Complex event processing Parameterized complexity 02 engineering and technology Solver Complex Event Processing computer.software_genre 01 natural sciences Task (project management) Set (abstract data type) 020204 information systems 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Data mining business computer Parametric statistics computer.programming_language |
| Zdroj: | BIRD: BCAM's Institutional Repository Data instname Advances in Intelligent Systems and Computing ISBN: 9789811037276 ICHSA |
| Popis: | 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 opti- mal value usually yields a challenging task. In this paper we explain a methodol- ogy 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 cer- tain use case – i.e. fuel level of the vehicle – are discussed towards assessing the performance gains provided by our method. |
| Databáze: | OpenAIRE |
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