Situation recognition using soft computing techniques

The last decades have witnessed the emergence of a large number of devices pervasively launched into our daily lives as systems producing and collecting data from a variety of information sources to provide different services to different users via a variety of applications. These include infrastructure management, business process monitoring, crisis management and many other system-monitoring activities. Being processed in real-time, these information production/collection activities raise an interest for live performance monitoring, analysis and reporting, and call for data-mining methods in the recognition, prediction, reasoning and controlling of the performance of these systems by controlling changes in the system and/or deviations from normal operation. In recent years, soft computing methods and algorithms have been applied to data mining to identify patterns and provide new insight into data. This thesis revisits the issue of situation recognition for systems producing massive datasets by assessing the relevance of using soft computing techniques for finding hidden pattern in these systems.

Grantor dc:publisher.institution

Department of Computer Science

Year dc:date.issued

2012

Author dc:creator

• Machaka, Pheeha

Advisor dc:contributor.advisor

• Bagula, Antoine

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/11427/11225

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/11225