An IoT-Enriched Event Log for Smart Factories with Injected Data Quality Issues

Modern technologies such as the Internet of Things (IoT) play a key role in Smart Manufacturing and Business Process Management (BPM). In particular, process mining benefits from enriched event logs that incorporate physical sensor data. This dataset presents an IoT-enriched XES event log recorded in a physical smart factory environment. It builds upon the previously published dataset “An IoT-Enriched Event Log for Process Mining in Smart Factories” (available on Zenodo) and follows the DataStream XES extension. In this modified version, three types of common Data Quality Issues (DQIs) - missing sensor values, missing sensors, and time shifts - have been artificially injected into the sensor data. These issues reflect realistic challenges in industrial IoT data processing and are valuable for developing and testing robust data cleaning and analysis methods. By comparing the original (clean) dataset with this modified version, researchers can systematically evaluate DQI detection, handling, and solving techniques under controlled conditions. Further details are provided for each of three DQI types in the subfolders in a csv changelog.诸如物联网（Internet of Things, IoT）在内的现代技术，在智能制造与业务流程管理（Business Process Management, BPM）领域发挥着核心作用。其中，流程挖掘尤为受益于融合物理传感器数据的增强型事件日志。本数据集提供了在实体智能制造工厂环境中记录的、融合物联网技术的XES事件日志。该数据集基于此前发布的《面向智能制造工厂流程挖掘的物联网增强型事件日志》（可在Zenodo平台获取）数据集，并遵循数据流XES扩展规范。在该修改版本中，研究人员已向传感器数据中人工注入三类常见数据质量问题（Data Quality Issues, DQIs）：传感器值缺失、传感器缺失与时间偏移。这些问题真实反映了工业物联网数据处理中的实际挑战，对于开发与测试鲁棒性的数据清洗与分析方法具有重要价值。研究人员可通过将原始（干净）数据集与该修改版本进行对比，在可控条件下系统性评估数据质量问题的检测、处理与解决技术。三类数据质量问题的详细说明均收录于各子文件夹内的CSV格式变更日志中。