# DataStream XES Extension: Embedding IoT Sensor Data into Extensible Event Stream Logs
## 2 Foundations and Related Work
### Process Mining
The dataset ([[https://doi.org/10.5281/zenodo.7411234](https://doi.org/10.5281/zenodo.7411234)]([https://doi.org/10.5281/zenodo.7411234](https://doi.org/10.5281/zenodo.7411234)) (accessed on 8 February 2023)) used for this section provides an example for the collection of such data in Vienna, Austria using (1) the API of the \"Wiener Linien\" (the company providing public transport in Vienna), (2) a weather API, and (3) the TomTom navigation system to monitor traffic. Data from these different sources is then collected by enacting a process in an execution engine and stored in the log using the proposed XES DataStream extension format. Using this information, the correlation between delays of individual tramways with the weather and traffic conditions near the track of the observed line can be investigated. This can then lead to better planning for smart city scenarios concerning traffic control in general or suggestions leading to an improvement of the public transportation system. Collecting data for the before mentioned scenario is done by enacting a process model using the cpee.org process execution engine. The process retrieves the individual data for weather, traffic, or tram line(s) (per endpoint providing the data) in parallel and then waits until the next minute starts to repeat the data collection. The process model in Figure 5 contains tasks that are responsible for the collection of data, which is signalized by the two curved lines on the top right side of these tasks. Such tasks contain a description of the data probes [43], which define how collected data is added to the log, as shown in Figures 6 and 7. These examples show how different kinds of data (in this case derived from the return value of the service invoked in this task) are extracted. It is possible to extract data in different ways, e.g., split data into multiple single data points (as shown in Figure 6) or create multiple data streams (as shown in Figure 7). Figure 5: Process Model for Collecting Public Transportation Delay Data (Process model used for creating the dataset described in [[https://doi.org/10.5281/zenodo.7411234](https://doi.org/10.5281/zenodo.7411234)]([https://doi.org/10.5281/zenodo.7411234](https://doi.org/10.5281/zenodo.7411234))) (accessed on 8 February 2023). Figure 6: Data Probes for the “Get Weather” Task. Corresponding snippets taken out of the created log are shown in Listings 6 and 7. Listing 6 contains a snippet of the weather data in the log as collected by the task shown in Figure 6. In this excerpt of the log, different stream:point elements (as specified in the data probes of the task) are contained in the stream:datastream element. So, (nested) stream:datastream elements can be used as a grouping mechanism that conveys semantic cohesiveness. For example, in Listing 8, different stops identified by a name and an id, are children of a \"traffic\" datastream. Instead of providing a flat list of values, additional semantic depth can be expressed, which can be utilized for visualization or analysis. Listing 7 contains a snippet of the log which is created by the task shown in Figure 7. The \"traffic\" data stream contains information about the traffic at multiple stops. The structure of the collected data is defined by the data probe defined in the task. In contrast to the other example, multiple data streams (stream:datastream) are created which each include one data point (stream:point) while in the first example just one data stream exists which includes multiple data points. Figure 7: Data Probe(s) for the “Get Traffic Status” Task. ### Manufacturing and Measuring of a Chess Piece at Pilotfabrik TU Wien

One goal in the manufacturing domain is the production of small lot sizes while still keeping a high degree of automation. To achieve this, it is possible to use a process execution engine enacting a process model consisting of several standardized subprocesseses.