A Technique for Determining Relevance Scores of Process Activities using Graph-based Neural Networks

A central role in process improvement is played by the process analyst [2], who is responsible for'monitoring, measuring, and providing feedback on the performance of a business process' [3, p.45]. The ongoing implementation of information systems in organisations, along with the subsequently enhanced availability of event log data, have enabled process analysts to discover as-is models of processes with process mining with relative ease [4]. However, the crucial challenge lies in identifying potential areas for process improvements (i.e., process analysis) with respect to a strategic goal [5]; this requires analytical capabilities such as Pareto or root cause analysis [2]. A business process can be defined as a'completely closed, timely, and logical sequence of activities' [6, p.3] that realises an outcome valuable to a customer [7]. The effectiveness (i.e., customer value) and efficiency (e.g., timely, logical sequence, resource utilisation) of a business process are monitored using key performance indicators (KPIs) as aggregated measures of process outcomes; in the context of BPM, these are often referred to as process performance indicators (PPIs) [8]. Thus, to improve a business process, it is essential for a process analyst to understand the relevance of individual process activities in terms of their impact on the dimensions expressed by these performance measures.