FM Maggi
Papers
2
Total Citations
45
H-Index
2
About
FM Maggi is a leading researcher at the intersection of Robotic Process Automation (RPA) and process mining, with a focus on enabling intelligent automation through data-driven discovery. His major contributions include pioneering multi-perspective process model discovery for RPA, which allows organizations to automatically identify and document repetitive tasks from user interface logs—dramatically reducing error rates and improving operational efficiency. His work on the Action Logger tool (2019, 16 citations) bridges a critical gap by generating UI logs suitable for process mining, effectively introducing advanced analytical methods to support RPA deployment. Maggi’s most cited paper (2018, 29 citations) established foundational techniques for extracting process models from RPA environments, directly impacting how firms approach cost reduction and data quality improvement. His research has been instrumental in transforming RPA from a simple task automation tool into a sophisticated, process-aware system. By combining process mining with automation, Maggi’s work continues to shape the future of digital workforce optimization, making him a key figure for students and researchers interested in the convergence of data science, business process management, and intelligent automation.
Research Focus
Key Achievements
Top Papers
- 1Multi-Perspective process model discovery for robotic process automation29 citations · 2018
- 2Action Logger: Enabling Process Mining for Robotic Process Automation16 citations · 2019