Situation recognition using soft computing techniques

Includes bibliographical references.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

A Comparative Evaluation of Business Intelligence Technologies with Application to Product Profiling

Most of the Business Intelligence tools available on the market today have either been developed and industrially operationalised as “one size fits all” solutions or offered with multiple options leaving the business to decide on the best technology to use. We infer that this approach is likely to result in various analysis inaccuracies; hence rendering inappropriate business decisions. Accordingly, evaluating which technologies present more accurate results against a particular business need remains imperative. While using customer data from a large financial services company in South Africa, we analysed the performance of Neural Networks, Artificial Immune Systems and Bayesian Networks in classifying customer buying patterns. We measured the accuracy percentage values for a customer’s propensity to buy policies and also for existing policies lapsing. We observed that such assessments provide great insight in assessing the effectiveness of Business Intelligence enabling technologies. In particular, when applied to a larger data set, various customer patterns can be unearthed which results in adequate customer segmentation and business lead optimisation