Papers
2
Total Citations
20
H-Index
2
About
Andreas Egger is a pioneering researcher at the intersection of Robotic Process Automation (RPA) and process mining. His work centers on developing novel methods to analyze and optimize automated business processes by leveraging execution logs generated by software robots. Egger’s major contribution is the concept of “bot log mining,” which integrates RPA log data with traditional process mining techniques to provide unprecedented visibility into how automated tasks are performed. His seminal 2020 paper, “Bot Log Mining: Using Logs from Robotic Process Automation for Process Mining,” has accumulated 16 citations, establishing the foundational framework for this emerging field. Egger’s approach enables organizations to detect inefficiencies, compliance issues, and improvement opportunities within their RPA implementations, effectively bridging the gap between automation execution and process analysis. His ongoing work, including a 2024 publication, continues to refine these integrated analysis methods, offering practical tools for enterprises seeking to maximize the value of their automation investments. Egger’s research is essential reading for anyone interested in the future of intelligent process optimization.
Research Focus
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Top Papers
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