The shift toward IoT-enabled, sensor-driven systems has transformed how operational data is generated, favoring continuous, real-time event streams (ES) over static event logs. This evolution presents new challenges for Streaming Process Mining (SPM), which must cope with out-of-order events, concurrent activities, incomplete cases, and concept drifts. Yet, the evaluation of SPM algorithms remains rooted in outdated practices, relying on static logs or artificially streamified data that fail to reflect the complexities of real-world streams. To address this gap, we first perform a comprehensive review of data stream literature to identify stream characteristics currently not reflected in the SPM community. Next, we use this information to extend the conceptual foundation for ES. Finally, we propose Stream of Intent, a prototype generator to produce ES with specific features. Our evaluation shows excellence in producing reproducible, intentional ES for targeted benchmarking and adaptive algorithm development in SPM.

翻译：向物联网赋能、传感器驱动系统的转变，已彻底改变运营数据的生成方式，连续实时的**事件流 (ES)** 正逐步取代静态事件日志。这一演变给**流式过程挖掘 (SPM)** 带来了新挑战，需应对乱序事件、并发活动、不完整案例及概念漂移等问题。然而，当前SPM算法的评估仍固守陈旧实践，依赖于静态日志或人工流化数据，无法反映真实世界数据流的复杂性。为填补该空白，我们首先对数据流文献进行全面综述，以识别当前SPM社区尚未体现的流特征；继而基于此拓展事件流的概念基础；最后提出**Stream of Intent**原型生成器，用于生成具有特定特征的事件流。评估表明，该方法在生成可复现、具意图的事件流方面表现卓越，可服务于SPM领域的精准基准测试与自适应算法开发。