DataStream XES Extension: Embedding IoT Sensor Data into Extensible Event Stream Logs

Abstract

The Internet of Things (IoT) has been shown to be very valuable for Business Process Management (BPM), for example, to better track and control process executions. While IoT actuators can automatically trigger actions, IoT sensors can monitor the changes in the environment and the humans involved in the processes. These sensors produce large amounts of discrete and continuous data streams, which hold the key to understanding the quality of the executed processes. However, to enable this understanding, it is needed to have a joint representation of the data generated by the process engine executing the process, and the data generated by the IoT sensors. In this paper, we present an extension of the event log standard format XES called DataStream. DataStream enables the connection of IoT data to process events, preserving the full context required for data analysis, even when scenarios or hardware artifacts are rapidly changing. The DataStream extension is designed based on a set of goals and evaluated by creating two datasets for real-world scenarios from the transportation/logistics and manufacturing domains.

NICE: The Native IoT-Centric Event Log Model for Process Mining

“…In the last years, several data formats have been adapted to IoT-enhanced event logs [9,20,28,22]. In this section, we present these new alternatives and compare them to our data format with respect to the requirements identified in [5].…”

“…In [20], authors present the XES DataStream extension, which aims at facilitating the storage of IoT data in XES logs. DataStream introduces, among others, the notions of point and datastream, which respectively represent individual sensor observations and group together points that belong to a same event or trace.…”

From Process-Agnostic to Process-Aware Automation, Mining, and Prediction

“…Moreover, PAWA systems can be employed to collect data in a systematic, integrated, and contextualized manner (→ Limitation 2), i.e., they log every event emitted during process execution and on top of that, PAWA systems can collect and log process context data, e.g., IoT data in domains such as production, health care, and logistics. The combined collection of process and IoT data has gained interest lately, resulting in an extension of the process event log standard eXtensible event stream (XES) 5 , i.e., the XES Sensor Stream extension [24]. This way, process engines and process-aware information systems serve as systems for the process-oriented and contextualized collection of process data at an arbitrary granularity (as defined in the process models) and a trusted, high quality level (****(*) star level according to the L * data quality model for process mining [3]) [30].…”