Abstract
Recent process mining techniques provide interesting new ways to uncover and comprehend complex work practices within organisations. The efficacy of process mining, however, is contingent upon the accessibility and quality of event logs. This paper introduces and describes a publicly-available dataset containing labelled Active Window Tracking data, capturing my app usage and active screen titles over the course of four weeks. It aims to support diverse studies: classifying activities from window titles, identifying action patterns, and developing new visualisations for detailed process data. By making this resource available, I aim to encourage the development of new process mining techniques that provide detailed insights into work practices.
Original language | English |
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Pages (from-to) | 61-65 |
Number of pages | 5 |
Journal | CEUR Workshop Proceedings |
Volume | 3758 |
Publication status | Published - 2024 |
Event | Best Dissertation Award, Doctoral Consortium, and Demonstration and Resources Forum at 22nd International Conference on Business Process Management, BPM-D 2024 - Krakow, Poland Duration: 1 Sept 2024 → 6 Sept 2024 |
Keywords
- Active Window Tracking
- cross-system data
- Event data
- UI logs