Pervasive computing devices are able to generate enormous amounts of distributed data, from which knowledge about situations and facts occurring in the world should be inferred for the use of pervasive services. However accessing and managing effectively such a huge amount of distributed information is challenging for services. In this paper we propose a self-organized agent-based approach to autonomously organize distributed contextual data items into sorts of knowledge networks. Knowledge networks are conceived as an alive selforganized layer in charge of managing data, that can facilitate services in extracting useful information out of a large amount of distributed items. We present the W4 Data Model we used to represent data and the self-organized approach to build Knowledge Networks. Some experimental results are reported to support our arguments and proposal. © 2009 IEEE.
Contextual data management and retrieval: A self-organized approach / Castelli, G.; Zambonelli, F.. - 3:(2009), pp. 535-538. (Intervento presentato al convegno 2009 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2009 tenutosi a Milano, ita nel 2009) [10.1109/WI-IAT.2009.343].
Contextual data management and retrieval: A self-organized approach
Zambonelli F.
2009
Abstract
Pervasive computing devices are able to generate enormous amounts of distributed data, from which knowledge about situations and facts occurring in the world should be inferred for the use of pervasive services. However accessing and managing effectively such a huge amount of distributed information is challenging for services. In this paper we propose a self-organized agent-based approach to autonomously organize distributed contextual data items into sorts of knowledge networks. Knowledge networks are conceived as an alive selforganized layer in charge of managing data, that can facilitate services in extracting useful information out of a large amount of distributed items. We present the W4 Data Model we used to represent data and the self-organized approach to build Knowledge Networks. Some experimental results are reported to support our arguments and proposal. © 2009 IEEE.Pubblicazioni consigliate
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