Applying Process Mining Algorithms in the Context of Data Collection Scenarios

Despite the technological progress, paper-based questionnaires are still widely used to collect data in many application domains like education, healthcare or psychology. To facilitate the enormous amount of work involved in collecting, evaluating and analyzing this data, a system enabling process-driven data collection was developed. Based on generic tools, a process-driven approach for creating, processing and analyzing questionnaires was realized, in which a questionnaire is defined in terms of a process model. Due to this characteristic, process mining algorithms may be applied to event logs created during the execution of questionnaires. Moreover, new data that might not have been used in the context of questionnaires before may be collected and analyzed to provide new insights in regard to both the participant and the questionnaire. This thesis shows that process mining algorithms may be applied successfully to process-oriented questionnaires. Algorithms from the three process mining forms of process discovery, conformance checking and enhancement are applied and used for various analysis. The analysis of certain properties of discovered process models leads to new ways of generating information from questionnaires. Different techniques for conformance checking and their applicability in the context of questionnaires are evaluated. Furthermore, new data that cannot be collected from paper-based questionnaires is used to enhance questionnaires to reveal new and meaningful relationships

Pre-hospital retrieval and transport of road trauma patients in Queensland: A process mining analysis

Existing process mining methodologies, while noting the importance of data quality, do not provide details on how to assess the quality of event data and how the identification of data quality issues can be exploited in the planning, data extraction and log building phases of any process mining analysis. To this end we adapt CRISP-DM [15] to supplement the Planning phase of the PM $$^2$$ [6] process mining methodology to specifically include data understanding and quality assessment. We illustrate our approach in a case study describing the detailed preparation for a process mining analysis of ground and aero-medical pre-hospital transport processes involving the Queensland Ambulance Service (QAS) and Retrieval Services Queensland (RSQ). We utilise QAS and RSQ sample data to show how the use of data models and some quality metrics can be used to (i) identify data quality issues, (ii) anticipate and explain certain observable features in process mining analyses, (iii) distinguish between systemic and occasional quality issues, and, (iv) reason about the mechanisms by which identified quality issues may have arisen in the event log. We contend that this knowledge can be used to guide the extraction, pre-processing stages of a process mining case study.</p