Using data-driven and process mining techniques for identifying and characterizing problem gamblers in New Zealand

This paper uses data-driven techniques combined with established theory in order to analyse gambling behavioural patterns of 91 thousand individuals on a real-world fixed-odds gambling dataset in New Zealand. This research uniquely integrates a mixture of process mining, data mining and confirmatory statistical techniques in order to categorise different sub-groups of gamblers, with the explicit motivation of identifying problem gambling behaviours and reporting on the challenges and lessons learned from our case study. We demonstrate how techniques from various disciplines can be combined in order to gain insight into the behavioural patterns exhibited by different types of gamblers, as well as provide assurances of the correctness of our approach and findings. A highlight of this case study is both the methodology which\ud demonstrates how such a combination of techniques provides a rich set of effective tools to undertake an exploratory and open-ended data analysis project that is guided by the process cube concept, as well as the findings themselves which indicate that the contribution that problem gamblers make to the total volume, expenditure and revenue is higher than previous studies have maintained

Inference of resource-based simulation models from process event-log data

This research was focused on inferring resource-based simulation models from data. and has proven it is realistic to do so. The research has discovered a new Process Mining algorithm with superior performance and has developed methods to identify, quantify and discover resource attributes and resource-based decisions from data