Process management and process orchestration/execution are currently hot topics; prevalent trends such as automation and Industry 4.0 require solutions which allow domain-experts to easily model and execute processes in various domains, including manufacturing and health-care. These domains, in turn, rely on a tight integration between hardware and software, i.e. via the Internet of Things (IoT). While process execution is about actuation, i.e. actively triggering actions and awaiting their completion, accompanying IoT sensors monitor humans and the environment. These sensors produce large amounts of procedural, discrete, and continuous data streams, that hold the key to understanding the quality of process subjects (e.g. produced parts), outcome (e.g. quantity and quality), and error causes. Processes constantly evolve in conjunction with their IoT environment. This requires joint storage of data generated by processes, with data generated by the IoT sensors is therefore needed. In this paper, we present an extension of the process log standard format XES, namely SensorStream. SensorStream enables to connect IoT data to process events, as well as a set of semantic annotations to describe the scenario and environment during data collection. This allows to preserve the full context required for data-analysis, so that logs can be analyzed even when scenarios or hardware artifacts are rapidly changing. Through additional semantic annotations, we envision the XES extension log format to be a solid based for the creation of a (semi-)automatic analysis pipeline, which can support domain experts by automatically providing data visualization, or even process insights.
翻译:流程管理和流程管弦化/执行目前是热题;自动化和工业4.0等流行趋势要求解决方案,让域专家能够轻松地在包括制造和保健在内的各个领域模拟和执行进程。这些领域反过来依赖于硬件和软件之间的紧密整合,即通过Times(IoT)互联网。虽然进程执行涉及激活,即积极触发行动和等待完成行动,伴有IoT传感器监测人类和环境。这些传感器产生大量程序、离散和连续数据流,从而能够将流程主题(例如,生成的部件)、结果(例如,数量和质量)和错误原因的质量自动理解。这些领域则依赖硬件和软件之间的紧密整合,即通过Times Internet环境(IoT传感器)进行。这需要联合存储流程生成的数据,同时需要IoT传感器生成的数据。在本文件中,我们展示了流程日志标准格式XES的扩展,即Sensor Stream。传感器能够将IoT数据连接到流程的直径流质量(例如,生成的部分)、结果(例如,质量)和误序背景分析情景分析中的数据,可以将预言中的数据转换成数据集。