提出可生成有目标意图的实时事件流，解决流程挖掘评估标准滞后问题。
Know Your Streams: On the Conceptualization, Characterization, and Generation of Intentional Event Streams
- 构建新型事件流生成器，支持可控特征的实时流数据
- 生成的事件流具备可复现性与明确意图，适合作为基准测试
- 适合研究流式流程挖掘算法与动态适应性系统的设计者
物联网与传感器系统的普及推动了持续、实时事件流（ES）取代静态事件日志成为主流数据形式。这一转变给流式流程挖掘（SPM）带来新挑战：需应对事件乱序、并发活动、案例不完整及概念漂移等问题。然而当前对SPM算法的评估仍依赖过时的静态日志或人为改造的流式数据，无法反映真实场景复杂性。为此，本文首先系统梳理数据流文献，识别出目前SPM领域尚未涵盖的流特性；其次，基于此拓展事件流的概念基础；最后，提出“意图流”（Stream of Intent）原型生成器，可生成具有特定特征的有意图事件流。评估表明，该方法能有效生成可复现、具目标性的事件流，适用于定向基准测试与自适应算法开发。
原文摘要 · Abstract (English)
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 Yet, the evaluation of SPM algorithms remains rooted in outdated practices, relying on static logs or artificially streamified data that fail to reflect the complexities of real-world streams.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.
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