## 7 Automated Event Visualization Using Semantic Reasoning
As a result, the event description is updated and the event is classified as a folio:ConfirmedAnomaly, indicating a user has validated the event. Depending on the user interaction, the event is also classified as a folio:RelabeledAnomaly, folio:Merged-Anomaly or folio:RejectedAnomaly. As a semantic ontology, FOLIO could be easily extended to fit the needs of specific use cases. For example, a folio-ext:ShowerEvent could be defined if specific functionality or visualizations were needed for shower events. Furthermore, if this class was defined as a subclass of folio:Anomaly, no other system would be impacted by this change. The descriptive labels assigned to events can be reused by the adaptive detectors described in Section 9. Figure 6 illustrates how the known pattern detector can automatically suggest many labels based on a small amount of patterns that were verified and labeled by the user. In this figure, we see the humidity of the bathroom over a period of 21 days. Red segments (annotated with \"A\") are suggested events detected by the unknown pattern detector from Section 5. After a user verifies and labels A0 and A4 as shower events (with A4 representing two consecutive showers), the known pattern detector will automatically recognize the orange segments (annotated with \"F\") as shower events because of the pattern-wise similarity. These matches will be reported as new events that are linked to the original event. In this short example, two interactions of the user in the first week of data led to a total of seventeen automatic labels. A demo movie (Available at [[https://www.youtube.com/watch?v=r7](https://www.youtube.com/watch?v=r7) ygntYFLxo]([https://www.youtube.com/watch?v=r7](https://www.youtube.com/watch?v=r7) ygntYFLxo), accessed on 11 July 2021) shows the full system in action and demonstrates this automated event labeling over a larger timespan. Therefore, with very limited feedback collected from the dashboard user, the detectors are able to automatically label a large amount of similar patterns. In contrast, without the detectors, a considerable amount of dashboard users would be required to try to cope with the data velocity, meaning that event labeling would be much more costly and many events would likely go undetected. Moreover, the relabeling of incorrectly detected events again \begin{table}
\begin{tabular}{c c} \hline \hline
**Event** & **Reasoning Time (s)** \\ \hline Unknown pattern & 0.452 (\(\pm\)0.012 std) \\ Semantic fault & 0.500 (\(\pm\)0.031 std) \\ Known pattern & 0.481 (\(\pm\)0.026 std) \\ Relabeled event & 0.457 (\(\pm\)0.011 std) \\ Merged event & 0.458 (\(\pm\)0.013 std) \\ Relabeled, merged event & 0.465 (\(\pm\)0.010 std) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Execution times, in seconds, required for the visualization reasoning of all types of events. leads to more precise labeling. The demonstrated symbiosis of dashboards for feedback capturing and different event detection techniques therefore enable label collection and correction on large datasets with only limited human intervention. ### Dynamic and Adaptive Solution End-to-End, Applicable to Other Use Cases

The expert knowledge and training data used in this work originate from a ventilation monitoring use case. Other use cases may of course benefit from a similar approach. We designed the system in such a way that components are generic solutions that can be easily reused for other use cases, i.e., the unified RDF-based event data model, the event-driven dashboard generation, feedback capturing and the architecture for streaming and permanent storage of events. In [PERSON] et al. [4], the system is applied on a train bogie monitoring case to detect train issues and wheel issues. In [PERSON] et al. [3], the event model, dashboard and overarching architecture are used for monitoring accelerated bearing lifetime tests, where the dashboard reports degradation in rolling elements bearings and displays their estimated remaining useful lifetime.