Mobile Cloud Context-Awareness System based on Jess Inference and Semantic Web RL for Inference Cost Decline


KIPS Transactions on Software and Data Engineering, Vol. 1, No. 1, pp. 19-30, Oct. 2012
10.3745/KTSDE.2012.1.1.19,   PDF Download:

Abstract

The context aware service is the service to provide useful information to the users by recognizing surroundings around people who receive the service via computer based on computing and communication, and by conducting self-decision. But CAS(Context Awareness System) shows the weak point of small-scale context awareness processing capacity due to restricted mobile function under the current mobile environment, memory space, and inference cost increment. In this paper, we propose a mobile cloud context system with using Google App Engine based on PaaS(Platform as a Service) in order to get context service in various mobile devices without any subordination to any specific platform. Inference design method of the proposed system makes use of knowledge-based framework with semantic inference that is presented by SWRL rule and OWL ontology and Jess with rule-based inference engine. As well as, it is intended to shorten the context service reasoning time with mapping the regular reasoning of SWRL to Jess reasoning engine by connecting the values such as Class, Property and Individual which are regular information in the form of SWRL to Jess reasoning engine via JessTab plug-in in order to overcome the demerit of queries reasoning method of SparQL in semantic search which is a previous reasoning method.


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Cite this article
[IEEE Style]
S. H. Jung and C. B. Sim, "Mobile Cloud Context-Awareness System based on Jess Inference and Semantic Web RL for Inference Cost Decline," KIPS Transactions on Software and Data Engineering, vol. 1, no. 1, pp. 19-30, 2012. DOI: 10.3745/KTSDE.2012.1.1.19.

[ACM Style]
Se Hoon Jung and Chun Bo Sim. 2012. Mobile Cloud Context-Awareness System based on Jess Inference and Semantic Web RL for Inference Cost Decline. KIPS Transactions on Software and Data Engineering, 1, 1, (2012), 19-30. DOI: 10.3745/KTSDE.2012.1.1.19.