# An Interactive Method for Detection of Process Activity Executions from IoT Data +
## 4 Method to Identify Activity Executions
### Zoom and Filter: Identify and Filter by Relevant CPS Components and Time Frames  
To reduce the complexity of the initial visualization, we propose to filter the data first by each CPS component to determine their relevance for the entire process execution. Then the data set has to be segmented into time frames per component to identify phases that should be analyzed further. As stated in Section 4.1, some of our basic assumptions are that the execution of an activity is limited to one CPS component and that a CPS component does not execute multiple activities in parallel. With these assumptions, we are able to determine the _relevance_ of a CPS component for the process execution as stated below. It is up to the analyst to decide the granularity level of the time-based segmentation of the data set. Recommendations here are to segment the data by periods of activity and inactivity per component according to the following definitions. This step does not need to completely rely on the domain expert but can be partially inferred automatically by analyzing the sensor/actuator changes following the activity/inactivity patterns. * If a CPS component does not exhibit significant changes in the values of its sensors or actuators for the time frame in question, it is considered _not relevant_ for the identification of activity executions. * If a CPS component exhibits significant changes in the values of its sensors or actuators for the time frame in question, it might be considered _relevant_ for the identification of activity executions. **Example 1**. : _Figure 6 shows the sensor and actuator values of the given data set filtered by the workstation transport (WT_1) component. Here we see that changes in the component's sensors and actuators only occur in the second part of the time frame (18:53-19:07). Thus, this component seems to be irrelevant for the first part of the process execution and relevant for the second part. Here we could divide the identified relevant time frame further into parts of activity and inactivity to make the segmentation more fine-grained. Given domain knowledge about the existing processes in the CPS (here: smart factory), we can additionally already derive that the activity executions in the first part of the data set might belong to the Storage Process since the WT_1 station is not involved here (cf. Section 2.2.2). Similarly, we can derive that the second part belongs to the Production Process with the involvement of the WT_1 station. Note that this is only possible in the example data set because there are only sequential process executions without overlap and a clear distinction between the first three instances (storage process, Section 2.2.2) and the second three instances (production process, Section 2.2.2)._  
**Example 2**. : _Figure 7 shows the sensor and actuator values of the given data set filtered by the high-bay warehouse (HBV_1) component. Here we see significant changes in its sensors and actuators during the entire time frame. Thus, the component might be relevant for the entire process execution. As the patterns look very similar and the HBV_1 component is part of both example processes, either to store a new workpiece (Storage Process) or to unload a workpiece (Production Process, cf. Section 2.2.2), we cannot make any statements about the types of processes the individual activities belong to._  
**Example 3**. : _Figure 8 shows the sensor and actuator values of the given data set filtered by the vacuum gripper robot (VGR_1) component. Being the central entity responsible for transportation, we see changes in this component's sensors and actuators and with that its relevance along the entire timeline. From the figure, we can also observe that patterns between the first part of the data set and the second part differ slightly, which might be an indication that they belong to different types of activities._  
Figure 6: IoT data filtered by the WT_1 (Workstation Transport) component. Figure 7: IoT data filtered by the HBW_1 (High-bay Warehouse) component. **Example 4**.