The field of process mining has evolved from discovering single work processes towards providing broad insights into peoples’ work practices. Existing techniques can be used to analyse such work practices, but this can be problematic if the available data is limited to the use of a single IT system or is not captured at the right level of granularity. We propose the use of a personal informatics technique, called Active Window Tracking (AWT), as a new way of gathering data for mining work practices. In this study, we identify the opportunities that this technique brings through a case study within our research group. In particular, we show how AWT helps to: capture previously-unrecorded work activities, expose the relations between work processes, and navigate between different levels of data granularity. The technique, which allows for generating new data as well as complementing existing data, is a valuable asset for the community when it comes to better understanding people’s work practices across individual systems and processes.

Predictive Process Monitoring aims to forecast the future progress of process instances using historical event data. As predictive process monitoring is increasingly applied in online settings to enable timely interventions, evaluating the performance of the underlying models becomes crucial for ensuring their consistency and reliability over time. This is especially important in high risk business scenarios where incorrect predictions may have severe consequences. However, predictive models are currently usually evaluated using a single, aggregated value or a time-series visualization, which makes it challenging to assess their performance and, specifically, their stability over time. This paper proposes an evaluation framework for assessing the stability of models for online predictive process monitoring. The framework introduces four performance meta-measures: the frequency of significant performance drops, the magnitude of such drops, the recovery rate, and the volatility of performance. To validate this framework, we applied it to two artificial and two real-world event logs. The results demonstrate that these meta-measures facilitate the comparison and selection of predictive models for different risk-taking scenarios. Such insights are of particular value to enhance decision-making in dynamic business environments.