AVOCADO: The Streaming Process Mining Challenge
This provides a standardized framework for algorithm evaluation in streaming process mining, which is incremental as it organizes existing metrics rather than introducing new methods.
The authors tackled the lack of standardized evaluation in streaming process mining by proposing AVOCADO, a challenge framework that separates concept and instantiation layers and evaluates algorithms on metrics like accuracy, MAE, RMSE, processing latency, and robustness.
Streaming process mining deals with the real-time analysis of streaming data. Event streams require algorithms capable of processing data incrementally. To systematically address the complexities of this domain, we propose AVOCADO, a standardized challenge framework that provides clear structural divisions: separating the concept and instantiation layers of challenges in streaming process mining for algorithm evaluation. The AVOCADO evaluates algorithms on streaming-specific metrics like accuracy, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Processing Latency, and robustness. This initiative seeks to foster innovation and community-driven discussions to advance the field of streaming process mining. We present this framework as a foundation and invite the community to contribute to its evolution by suggesting new challenges, such as integrating metrics for system throughput and memory consumption, and expanding the scope to address real-world stream complexities like out-of-order event arrival.