# DataStream XES Extension: Embedding IoT Sensor Data into Extensible Event Stream Logs
## 2 Foundations and Related Work
### Process Mining
Limitations currently exist regarding the usability of the data structure in analysis tools, as there are no implementations to use it. Moreover, due to the large amounts of data in the IoT context, the logs can quickly become very large, which might overwhelm some existing process mining tools. ## 5 Conclusions and Future Work

In this paper, an extension to XES has been presented, allowing the joint representation of process event logs and IoT data related to the environment where these events occur. This enables the development of generic data pipelines, process mining approaches, and visualization tools for IoT event logs. The extension identifies what is required from the IoT perspective to enable the use of BPM methods for IoT (cf. [4]). As it has been shown, there are special requirements for the data perspective in the IoT context, especially regarding IoT sensor data. Section 1 defines goals which should be met by the proposed XES extension. By describing real-world application scenarios (see Section 4) and demonstrating how the goals are achieved using the DataStream XES extension proposed in this paper, it is proven that the approach fulfills the set requirements:

* _\"Provide a well-defined set of named XES attributes to describe individual (sensor) events.\"_ is achieved by defining the named XES attributes in the DataStream Metamodel shown in Figure 4 and using these attributes in the logs of the real-world application scenarios. * _\"Utilize well-established XES concepts such as lists to group the named attributes for simplified analysis.\"_ is achieved by identifying different granularity levels such as stream:datastream and point in the DataStream Metamodel shown in Figure 4 and assign the named attributes to their corresponding levels. This leads to easier analysis as for example stream:points can share attributes of their parent stream:datstream as shown in Listings 7-9. This serves the purpose of data de-duplication for a set of stream:point attributes, thus reducing data cleaning effort. * _\"Establish a set of named XES attributes to store many (sensor) events per process event.\"_ is achieved by allowing for multiple stream:datstreams and therefore also stream:points to be connected to one process event in the DataStream Metamodel shown in Figure 4. This is also shown in Listings 6-9 where multiple stream:datstreams and/or stream:points are present in one process event. Having a set of common attributes instead of a mix of different attributes with slightly different meaning, reduces data transformation effort, and provides context and meaning to the most basic shared concepts. * _\"Describe how to store large quantities of (sensor) events, which might occur between the start/end of a process event or a process instance (i.e., establishing a new XES BPAF lifecycle transition).\"_ is achieved by introducing the stream/data lifecycle transition. The stream/data lifecycle transition allows us to add IoT data to the XES log at any time therefore omitting the need to wait for the next process event to carry the data collected in the process until then. This is described in more depth in Section 3. * _\"Establish a set of named XES attributes to connect (sensor) events to groups of process tasks.\"_ is achieved by introducing stream/datacontext (see Section stream/datacontext). This allows us to connect (sensor) data to process tasks, although it is not in the same granularity, for example when averaged sensor readings (e.g., temperature, humidity,...) span multiple process tasks. In the future, a complete event log of a factory shall be parsed, analysed and visualized based on the proposed extension format to support process refinement and root cause analysis, in order to promote process re-engineering with the goal of resilient [54] shop floor processes. Furthermore, tools for the visualization of DataStream-based event logs are to be developed and existing process mining approaches are to be adapted in such a way that they can also process the DataStream extension.