The paper "Fuzzy Mining - Adaptive Process Simplification Based on Multi-perspective Metrics" by Christian W. Günther and Wil M.P. van der Aalst addresses the challenges of process mining in less-structured environments. Traditional process mining techniques often produce "spaghetti-like" models that are difficult to interpret and lack meaningful abstraction. The authors propose a new approach called Fuzzy Mining, which is configurable and allows for different simplified views of a process. The approach is inspired by the concept of a roadmap, where only the most significant and highly correlated aspects of the process are retained, while less significant and less correlated aspects are abstracted or aggregated. The paper introduces two key metrics—significance and correlation—and a set of log-based process metrics to measure these. The simplification process involves conflict resolution, edge filtering, and node aggregation and abstraction. The Fuzzy Miner plugin for the ProM framework implements these techniques, and the authors demonstrate its effectiveness through various case studies. The approach aims to balance the need for precise modeling with the goal of providing understandable, high-level information, making process mining more applicable in practical settings.The paper "Fuzzy Mining - Adaptive Process Simplification Based on Multi-perspective Metrics" by Christian W. Günther and Wil M.P. van der Aalst addresses the challenges of process mining in less-structured environments. Traditional process mining techniques often produce "spaghetti-like" models that are difficult to interpret and lack meaningful abstraction. The authors propose a new approach called Fuzzy Mining, which is configurable and allows for different simplified views of a process. The approach is inspired by the concept of a roadmap, where only the most significant and highly correlated aspects of the process are retained, while less significant and less correlated aspects are abstracted or aggregated. The paper introduces two key metrics—significance and correlation—and a set of log-based process metrics to measure these. The simplification process involves conflict resolution, edge filtering, and node aggregation and abstraction. The Fuzzy Miner plugin for the ProM framework implements these techniques, and the authors demonstrate its effectiveness through various case studies. The approach aims to balance the need for precise modeling with the goal of providing understandable, high-level information, making process mining more applicable in practical settings.