Situation recognition using soft computing techniques

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dc.contributor.advisorBagula, Antoineen_ZA
dc.contributor.authorMachaka, Pheehaen_ZA
dc.date.accessioned2015-01-03T18:31:30Z
dc.date.available2015-01-03T18:31:30Z
dc.date.issued2012en_ZA
dc.descriptionIncludes bibliographical references.en_ZA
dc.description.abstractThe 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.en_ZA
dc.identifier.apacitationMachaka, P. (2012). <i>Situation recognition using soft computing techniques</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Computer Science. Retrieved from http://hdl.handle.net/11427/11225en_ZA
dc.identifier.chicagocitationMachaka, Pheeha. <i>"Situation recognition using soft computing techniques."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Computer Science, 2012. http://hdl.handle.net/11427/11225en_ZA
dc.identifier.citationMachaka, P. 2012. Situation recognition using soft computing techniques. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Machaka, Pheeha AB - 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. DA - 2012 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2012 T1 - Situation recognition using soft computing techniques TI - Situation recognition using soft computing techniques UR - http://hdl.handle.net/11427/11225 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/11225
dc.identifier.vancouvercitationMachaka P. Situation recognition using soft computing techniques. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Computer Science, 2012 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/11225en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Computer Scienceen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherComputer Scienceen_ZA
dc.titleSituation recognition using soft computing techniquesen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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