JUCS - Journal of Universal Computer Science 11(8): 1440-1453, doi: 10.3217/jucs-011-08-1440
Resource-aware Mining of Data Streams
expand article infoMohamed Medhat Gaber‡, Shonali Krishnaswamy‡, Arkady Zaslavsky‡
‡ Centre for Distributed Systems and Software Engineering, Monash University, Australia

Corresponding author: Mohamed Gaber ( mohamed.medhat.gaber@infotech.monash.edu.au )

© Mohamed Gaber, Shonali Krishnaswamy, Arkady Zaslavsky.
This article is freely available under the J.UCS Open Content License.
Citation: Gaber MM, Krishnaswamy S, Zaslavsky A (2005) Resource-aware Mining of Data Streams. JUCS - Journal of Universal Computer Science 11(8): 1440-1453. https://doi.org/10.3217/jucs-011-08-1440
Open Access
Abstract
Mining data streams has raised a number of research challenges for the data mining community. These challenges include the limitations of computational resources, especially because mining streams of data most likely be done on a mobile device with limited resources. Also due to the continuality of data streams, the algorithm should have only one pass or less over the incoming data records. In this article, our Algorithm Output Granularity (AOG) approach in mining data streams is discussed. AOG is a novel adaptable approach that can cope with the challenging inherent features of data streams. We also show the results for AOG based clustering in a resource constrained environment.
Keywords
data mining, data stream, clustering, resource-aware computing