Computer
Some computer science issues in ubiquitous computing
Communications of the ACM - Special issue on computer augmented environments: back to the real world
The fading concept in tuple-space systems
Proceedings of the 2006 ACM symposium on Applied computing
Design patterns from biology for distributed computing
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Case studies for self-organization in computer science
Journal of Systems Architecture: the EUROMICRO Journal - Special issue: Nature-inspired applications and systems
A Simple Model and Infrastructure for Context-Aware Browsing of the World
PERCOM '07 Proceedings of the Fifth IEEE International Conference on Pervasive Computing and Communications
Engineering contextual knowledge for autonomic pervasive services
Information and Software Technology
The LighTS tuple space framework and its customization for context-aware applications
Web Intelligence and Agent Systems
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Pervasive computing devices (e.g., sensor networks, localization devices, cameras, etc.) are increasingly present in every aspect of our lives. These devices are able to generate enormous amounts of data, from which knowledge about situations and facts occurring in the world can be inferred; inference can also be done by combining data items and generating new (higher-level) ones. Such data and knowledge is of extreme importance for to context-aware and mobile services. However, we are left with the problem that the possibly huge amount of data and knowledge generated can be very hard to be analyzed and made usable in real-time. The core of the problem in today's pervasive environments lies between the ability to extract meaningful (useful) knowledge from the data while making sure the total amount of data does not become overwhelming to the system. This paper focus on this trade-off using (without loss of generality) the W4 model for contextual data as a case study. Starting from the basic mechanism by which the W4 model autonomously generate new knowledge, the paper shows how this can generate knowledge overflow, and propose a method to select---in a self-organizing way---what kinds of knowledge should be generated based on their importance; hence preventing knowledge overflow. Experimental results are reported to support our arguments and proposals.