AVOCADO：流式过程挖掘挑战赛 (AVOCADO: The Streaming Process Mining Challenge)

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.

翻译：流式过程挖掘致力于对实时流数据进行即时分析。事件流要求算法具备增量式处理数据的能力。为系统性地应对该领域的复杂性，我们提出AVOCADO——一个标准化的挑战框架，该框架通过分离流式过程挖掘算法评估中挑战的概念层与实例层，提供了清晰的结构化划分。AVOCADO基于流式场景特有的指标对算法进行评估，包括准确率、平均绝对误差（MAE）、均方根误差（RMSE）、处理延迟及鲁棒性。本倡议旨在促进创新与社区驱动的讨论，以推动流式过程挖掘领域的发展。我们提出此框架作为基础，并邀请社区通过建议新增挑战（例如整合系统吞吐量与内存消耗的度量指标，以及扩展研究范围以应对乱序事件到达等实际流数据复杂性）共同推动其演进。