CEML: Mixing and moving complex event processing and machine learning to the edge of the network for IoT applications


The Internet of Things (IoT) is a growing field which is expected to generate and collect data everywhere at any time. Highly scalable cloud analytics systems are frequently being used to handle this data explosion. However, the ubiquitous nature of the IoT data imposes new technical and non-technical requirements which are difficult to address with a cloud deployment. To solve these problems, we need a new set of development technologies such as Distributed Data Mining and Ubiquitous Data Mining targeted and optimized towards IoT applications.

In this paper, we present the Complex Event Machine Learning framework which proposes a set of tools for automatic distributed machine learning in (near-) real-time, automatic continuous evaluation tools, and automatic rules management for deployment of rules. These features are implemented for a deployment at the edge of the network instead of the cloud. We evaluate and validate our approach with a well-known classification problem.

Type of publication: 
Publication in conference proceedings
ISSN or eSSN: 
Jose Angel Carvajal Soto, Marc Jentsch, Davy Preuveneers, Elisabeth Ilie-Zudor
Journal or equivalent: 
Proceedings of the 6th International Conference on the Internet of Things (IoT2016)
Number, date: 
November 7–9
Year of publication: 
Relevant pages: 
.to be defined
Place of publication: 
Stuttgart, Germany