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Self-organized Data Ecologies for Pervasive Situation-Aware Services: the Knowledge Networks Approach

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Bicocchi, Nicola, Baumgarten, Matthias, Brgulja, Nermin, Kusber, Rico, Mamei, Marco, Mulvenna, Maurice and Zambonelli, Franco (2010) Self-organized Data Ecologies for Pervasive Situation-Aware Services: the Knowledge Networks Approach. IEEE Transactions on Systems, Man, and Cybernetics, Part A; Systems and Humans, 4 (40). pp. 789-802. [Journal article]

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DOI: 10.1109/TSMCA.2010.2048023

Abstract

Pervasive computing services exploit information about the physical world both to adapt their own behavior in a context-aware way and to deliver to users enhanced means of in- teraction with their surrounding environment. The technology to acquire digital information about the physical world is becoming more available, making services at risk of being overwhelmed by such growing amounts of data. This calls for novel approaches to represent and automatically organize, aggregate, and prune such data before delivering them to services. In particular, individual data items should form a sort of self-organized ecology in which, by linking and combining with each other into sorts of “knowledge networks” (KNs), they are able to provide compact and easy- to-be-managed higher level knowledge about situations occurring in the environment. In this context, the contribution of this paper is twofold. First, with the help of a simple case study, we motivate the need to evolve from models of “context awareness” toward models of “situation awareness” via proper self-organized “KN” tools, and we introduce a general reference architecture for KNs. Second, we describe the design and implementation of a KN toolkit that we have developed, and we exemplify and evaluate algorithms for knowledge self-organization integrated within it. Open issues and future research directions are also discussed.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
ID Code:14245
Deposited By:Professor Maurice Mulvenna
Deposited On:23 Jun 2010 16:46
Last Modified:23 Jun 2010 16:46

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