# An Interactive Method for Detection of Process Activity Executions from IoT Data +
## 6 Discussion
### Applicability of the Method in Other IoT Domains  
We have derived the method for activity detection from IoT data based on the comprehensive, exploratory analysis of a data set from the domain of smart manufacturing. As seen in the example setup (cf. Section 2.2.1), CPS in this domain usually contain a rich set of sensors and actuators that generate low-level (raw) IoT data. This environment provides a suitable basis for deriving sensor/actuator patterns and an analysis method that is applicable more generally, which was the goal of our work. The patterns and their composition presented in Section 4.4.1 refer to _sensors_ and _actuators_ in general, which are not specific to the domain of smart manufacturing but can be found in every IoT domain [10]. The derived method addresses the analysis of data from sensors and actuators independent of a specific domain. Hence, we expect the method to be applicable in other IoT domains.  
While the low-level IoT data in smart manufacturing is usually complemented by higher-level status and process information coming from the PLCs and MES that may help with the detection of automated process activities (cf. Section 2.2), other IoT domains (e.g., smart homes, smart offices, smart healthcare) only feature a smaller set of sensors and actuators that produce low-level IoT data. We expect that the proposed method and sensor/actuator patterns may prove to be even more valuable in these IoT domains relying only on a limited amount of sensors and actuators to enable the monitoring of activity  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Step** & **Analyst Decision** & \
\begin{tabular}{c} **Domain** \\ **Knowledge** \\ \end{tabular} &
\begin{tabular}{c} **Automation Potential** \\ \end{tabular} \\ \hline
1: Visualize all IoT data over time & – & – & not necessary (only to support the analyst) \\ \hline
2: Identify relevant CPS components and time frames & relevance of CPS components & characteristics of CPS components, sensors and actuators; process knowledge & _high_: detect areas of sensor/actuator changes \\ \hline
3: Filter by CPS component and time frame & – & – & full automation \\ \hline
4: Find activity start and end pattern of activities; level of granularity; activity label & characteristics of CPS components, sensors and actuators; process knowledge & _low_: find general patterns (cf. Table 4), calculate sensor/actuator dependencies for new patterns _limitations_: interpretation of calculated dependencies, domain-specific patterns, unknown level of activity granularity and activity labels, insufficient IoT data \\ \hline
5: Determine activity signature & – & – & full automation \\ \hline
6: Find and label similar activities & similarity threshold & process knowledge & _very high_: find similarities in time series data _limitations_: varying similarity thresholds, ambiguities of activity labels \\ \hline
7: Visualize all detected activities & – & – & not necessary (only to support the analyst) \\ \hline
8: Find repeated activity sequences &
\begin{tabular}{c} loops; start and end of one process instance; activity–instance correlation \\ \end{tabular} & process knowledge & _high_: find repeated sequences _limitation_: activity–instance correlation \\ \hline \hline \end{tabular}
\end{table}
Table 6: Manual steps in the method and automation potential.  
executions. The application of the proposed method for activity detection in various case studies in other smart spaces remains subject to future work.