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Title: CEML: Mixing and moving complex event processing and machine learning to the edge of the network for IoT applications
Authors: Soto, Jose Angel Carvajal
Jentsch, Marc
Preuveneers, Davy
Ilie-Zudor, Elisabeth
Issue Date: Nov-2016
Publisher: ACM
Host Document: Proceedings of the 6th International Conference on the Internet of Things (IoT'16) pages:103-110
Conference: International Conference on the Internet of Things edition:6 location:Stuttgart, Germany date:07-09 November 2016
Abstract: 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.
ISBN: 978-1-4503-4814-0
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section

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