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式数据进行在线分析。事件流需要具备增量数据处理能力的算法。为系统应对该领域的复杂性，我们提出AVOCADO标准化挑战框架，该框架提供明确的结构划分：将流式过程挖掘算法评估中的概念层与实例化层挑战进行分离。AVOCADO使用准确率、平均绝对误差（MAE）、均方根误差（RMSE）、处理延迟和鲁棒性等流式专用指标评估算法。本倡议旨在推动创新和社区驱动的讨论，以促进流式过程挖掘领域的发展。我们将此框架作为基础，诚邀学界同仁通过提出新挑战（如整合系统吞吐量和内存消耗指标）及扩展研究范围（应对乱序事件到达等真实流式数据的复杂性）共同推动其演进。