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Analysis of the Feasibility to Combine CEP and EDA with Machine Learning using the Example of Network Analysis and Surveillance


Complex Event Processing (CEP) and Event-driven Architectures (EDA) are modern paradigms for processing data in form of events. Machine Learning (ML) methods offer additional sophisticated means for analyzing data. By combining these technologies it is possible to create even more comprehensive and powerful data analysis and processing systems. We analyze the feasibility of combining CEP and EDA with ML using the example of the application domain of computer networks. We present relevant aspects, a sample use case, an sample architecture, and results of performance benchmarks. Our results indicate that the combination of these technologies increases data processing capabilities and that it is feasible from a performance perspective as well.

Palabras Clave:

Complex Event Processing - Computer Networks - Event-driven Architecture - Machine Learning





Este artículo tiene una licencia de uso CreativeCommons - Reconocimiento (by)

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