HeuristikMiner

HeuristikMiner is a conceptual framework and a set of algorithms used in process mining for discovering process models. It focuses on identifying the most probable process flows from event logs, prioritizing common paths and frequently occurring activities. Unlike more exhaustive discovery techniques that aim to find all possible paths, HeuristikMiner uses heuristic choices to simplify the resulting process model, making it more understandable and manageable, especially for complex processes with many variations.

The core idea behind HeuristikMiner is to leverage heuristics to filter out infrequent or less significant paths, thereby reducing the complexity of the discovered process model. It typically involves calculating a "fitness" measure for different transitions between activities, indicating how well a particular path explains the observed events. Transitions with higher fitness scores are more likely to be included in the final model. This approach is particularly useful when dealing with large and noisy event logs, where a complete discovery might be overwhelming or inaccurate due to infrequent, erroneous, or exceptional behaviors.

The output of HeuristikMiner is often a simplified Petri net or a similar process representation that highlights the main execution paths. This makes it easier for domain experts to validate the model, identify bottlenecks, and understand the dominant behavior within a process. While it sacrifices some completeness for clarity, HeuristikMiner is a valuable tool for gaining practical insights into real-world business processes.