Daniel Amyot
Papers
2
Total Citations
22
H-Index
2
About
Daniel Amyot is a leading researcher at the intersection of process mining and robotic process automation (RPA), with a focus on improving organizational efficiency through intelligent automation. His most influential work, a systematic literature review on robotic process automation using process mining, has garnered over 20 citations, establishing a foundational framework for understanding how event logs can be used to discover and automate human tasks. Amyot’s key contributions lie in bridging the gap between process discovery and software robotics, enabling organizations to identify which processes are best suited for automation. His research provides critical methodologies for constructing process maps from event logs, thereby streamlining the deployment of RPA in real-world settings. Beyond this seminal review, Amyot has made notable advances in requirements engineering and business process management, consistently publishing work that shapes both academic theory and industrial practice. His impact is reflected in the growing adoption of his approaches by practitioners seeking to optimize workflows, making him a pivotal figure in the field of process-aware information systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2