Automate It All!
Revamping the Outsourcing Industry
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Beschreibung
This book is a revised version of the PhD dissertation written by the author at the University of Seville, Spain.
It proposes a task mining framework that enriches user interface (UI) logs by incorporating visual information through screenshots and eye-tracking data. This approach enables task mining in restrictive industrial environments, such as the secured or virtualized connections common in outsourcing, overcoming the technical limitations of traditional loggers. Beyond merely identifying activities, the framework enables the extraction of underlying decision models to accurately capture the 'as-is' execution of tasks. Consequently, it allows for the construction of decision trees that explain user choices in greater depth, significantly accelerating the analysis of processes targeted for automation. The proposed framework has been validated through a case study involving synthetic mockups and real-life screenshots, demonstrating both a high level of accuracy in capturing user decisions and its practical usefulness in real-world automation initiatives.
This book is a revised version of the PhD dissertation written by the author at the University of Seville, Spain.
It proposes a task mining framework that enriches user interface (UI) logs by incorporating visual information through screenshots and eye-tracking data. This approach enables task mining in restrictive industrial environments, such as the secured or virtualized connections common in outsourcing, overcoming the technical limitations of traditional loggers. Beyond merely identifying activities, the framework enables the extraction of underlying decision models to accurately capture the 'as-is' execution of tasks. Consequently, it allows for the construction of decision trees that explain user choices in greater depth, significantly accelerating the analysis of processes targeted for automation. The proposed framework has been validated through a case study involving synthetic mockups and real-life screenshots, demonstrating both a high level of accuracy in capturing user decisions and its practical usefulness in real-world automation initiatives.
Won the BPM 2025 PhD award for an outstanding theses in the field of business process management Proposes a framework that improves the identification of process activities and enables the discovery of decision models Results were validated through multiple case studies demonstrating a high level of accuracy in capturing user decisions
Autor*in
Antonio Martínez Rojas
Themen in »Automate It All!«
process science agentic and process automation business process management task mining real-world challenges process discovery RPA - robotic process automation process mining outsourcing automation workflow management automation eye-tracking technology