DSpace Repository

Intelligent risk profiling for project management

Show simple item record

dc.contributor.advisor Fourie CJ, Mr en
dc.contributor.author Loftus K en
dc.date.accessioned 2016-09-22T08:34:30Z
dc.date.available 2016-09-22T08:34:30Z
dc.date.submitted 2003 en
dc.identifier.uri http://hdl.handle.net/20.500.11892/30994
dc.description.abstract Whenever projects fail, analysis of the causes has shown that risks were present from day one. Often individuals at some level in the project team have knowledge of these risks and they could have been identified and appropriate remedial action taken. Risk, whether identified or not, generally results in some increase in financial exposure on behalf of the organisation, but, if managed well, offers a potential that could lead to increased profits. There has been a tremendous explosion regarding the amount of data that organisations generate, collect and store. Managers are beginning to recognize the value of this asset and are increasingly relying on intelligent systems to access, analyse, summarise and interpret information from large and multiple data sources. These systems help them to make critical decisions at a faster rate or with a greater degree of confidence. Data mining is a promising new technology that helps bring intelligence into these systems. The purpose of this thesis is to present a methodology that integrates a data mining technique with a decision support system in order to form an intelligent decision support system. The implementation of such an intelligent decision support system will enable project and project risk managers to improve the management of and reduce risk within a project. This thesis consists of two sections. The first section describes the processes and characteristics of project management, project risk management, and data mining and decision support systems. The aim is to provide the reader with a background about these four management methodologies. The second section describes the methodology of how the processes of project and project risk management can benefit from the integration of a data mining technique and a decision support system. An application that uses the case-based reasoning approach as a data mining technique to intelligently profile a project according to its risks is demonstrated. en
dc.language English en
dc.subject Business economics en
dc.subject General management en
dc.title Intelligent risk profiling for project management en
dc.type Masters degree en
dc.description.degree MEng en


Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record