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

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 interaction with their surrounding environment. The technology to acquire digital information about the physical world is increasingly 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 growing amounts of data before delivering it 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”, they can be able to provide to services 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” towards models of “situation-awareness” via proper self-organized “knowledge networks” tools, and introduce a general reference architecture for knowledge networks. Second, we describe the design and implementation of a knowledge network toolkit we have developed, and exemplify algorithms for knowledge self-organization integrated within it. Open issues and future research directions are also discussed