Article: Efficient filtering processes for machine-to-machine data based on automation modules and data-agnostic algorithms Journal: International Journal of Business Process Integration and Management (IJBPIM) 2014 Vol.7 No.1 pp.73 - 86 Abstract: Machine-to-machine (M2M) platforms are evolving as large-scale multi-layer solutions that unify the access and the control of all devices that are being equipped with the capability to perform automated tasks and to report data based on connectivity to a backend system. As the integration of more and more devices in such platforms results in the need to handle big M2M data, M2M platforms need to automate their configuration and include appropriate data filtering frameworks and algorithms. Otherwise, the collected raw data can become expensive, unmanageable, and of low quality. This paper presents how data filtering processes can be automated as part of an M2M self-configuration framework and describes a solution that enables the seamless adjustment of domain-specific filtering thresholds in domain-agnostic platforms, based on quality-of-information calculations and M2M-specific data categorisation. An evaluation from the facilities-monitoring domain shows that our approach was the only one to achieve, for example, forwarding less than 25% of the monitored data maintaining at the same time a coverage ratio bigger than 50% for all considered applications. Further, a projection of this evaluation to a Smart City scale indicates that such gains can make database queries up to many seconds faster. Inderscience Publishers - linking academia, business and industry through research

Title: Efficient filtering processes for machine-to-machine data based on automation modules and data-agnostic algorithms

Authors: Apostolos Papageorgiou; Mischa Schmidt; JaeSeung Song; Nobuharu Kami

Addresses: NEC Laboratories Europe, Kurfürstenanlage 36, 69115 Heidelberg, Germany ' NEC Laboratories Europe, Kurfürstenanlage 36, 69115 Heidelberg, Germany ' Sejong University, Department of Computer and Information Security, Gwangjin-Gu, Seoul, 143-747, Korea ' NEC Corporation, 1753 Shimonumabe, Nakahara-ku, Kawasaki, Kanagawa 211-8666, Japan

Abstract: Machine-to-machine (M2M) platforms are evolving as large-scale multi-layer solutions that unify the access and the control of all devices that are being equipped with the capability to perform automated tasks and to report data based on connectivity to a backend system. As the integration of more and more devices in such platforms results in the need to handle big M2M data, M2M platforms need to automate their configuration and include appropriate data filtering frameworks and algorithms. Otherwise, the collected raw data can become expensive, unmanageable, and of low quality. This paper presents how data filtering processes can be automated as part of an M2M self-configuration framework and describes a solution that enables the seamless adjustment of domain-specific filtering thresholds in domain-agnostic platforms, based on quality-of-information calculations and M2M-specific data categorisation. An evaluation from the facilities-monitoring domain shows that our approach was the only one to achieve, for example, forwarding less than 25% of the monitored data maintaining at the same time a coverage ratio bigger than 50% for all considered applications. Further, a projection of this evaluation to a Smart City scale indicates that such gains can make database queries up to many seconds faster.

Keywords: machine-to-machine; M2M data; data filtering; autonomic computing; Big Data; M2M self-configuration; smart cities; facilities monitoring; data monitoring.

DOI: 10.1504/IJBPIM.2014.060606

International Journal of Business Process Integration and Management, 2014 Vol.7 No.1, pp.73 - 86

Published online: 31 Jul 2014 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: Efficient filtering processes for machine-to-machine data based on automation modules and data-agnostic algorithms

Keep up-to-date