A framework for resource-aware knowledge discovery in data streams: A holistic approach with its application to clustering

Abstract

Mining data streams is a field of increase interest due to the importance of its applications and dissemination of data stream generators. Most of the streaming techniques developed so far have not addressed the need of resource-aware computing in data stream analysis. The fact that streaming information is often generated or received onboard resource-constrained computational devices such as sensors and mobile devices motivates the need for resource-awareness in data stream processing systems. In this paper, we propose a generic framework mat enables resource-awareness in streaming computation using algorithm granularity settings in order to change the resource consumption patterns periodically. This generic framework is applied to a novel threshold-based micro-clustering algorithm to test its validity and feasibility. We have termed this algorithm as RA-Cluster. RA-Custer is the first stream clustering algorithm that can adapt to the changing availability of different resources. The experimental results showed the applicability of the framework and the algorithm in terms of resource-awareness and accuracy. Copyright 2006 ACM.