Efficient filtering processes for machine-to-machine data based on automation modules and data-agnostic algorithms
by Apostolos Papageorgiou; Mischa Schmidt; JaeSeung Song; Nobuharu Kami
International Journal of Business Process Integration and Management (IJBPIM), Vol. 7, No. 1, 2014

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.

Online publication date: Thu, 31-Jul-2014

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Business Process Integration and Management (IJBPIM):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com