## 7 Automated Event Visualization Using Semantic Reasoning
In our experiment, we achieved a throughput rate of 400 measurement values per second, where a normal throughput would be 16 measurement values per minute. As

Figure 4: Overview of the interplay of event detectors (colored, thin border) and the dashboarding platform (grey, thick border). Data-driven detectors are displayed in blue (solid border), knowledge-driven in green (dashed border). Arrows indicate the flow direction of information. the event data comes in at a much lower rate than the measurements, it has little effect on the total throughput rate. In summary, this architecture succeeds in combining event detection techniques, knowledge-driven as well as data-driven, with dynamic dashboarding for feedback capturing, under high load of streaming data representative for real-time ventilation monitoring in the field. ### Automated and Fast Event-Driven Dashboard Creation

Companies will welcome any reduction of human effort required to monitor the sheer amount of assets and event alerts, as this will reduce costs and will help experts focus more on the monitoring task itself. Therefore, the second objective in this paper was attaining fully automated and rapid dashboard creation for events selected for further investigation by an expert. Contrary to earlier design of the dynamic dashboard, as published in [PERSON] et al. (2015), event visualization is done using semantic reasoning as described in Section 7 and no longer requires the dashboard user to manually select, per widget, a visualization from a set of well-matching candidate visualizations. The number of candidate visualizations for all widgets on the event dashboard tab is systematically reduced by subsequent reasoning over all rules expressed in the visualization logic. Eventually, either only a single candidate visualization remains, or additional rules at the end of the reasoning process select the candidate visualization that is used most frequently among all dashboard operators. To achieve good user experience, it is important that the delay required to construct the event dashboard is below one second. The semantic reasoning involved is the most complex step in this process. Table 1 shows the execution times of the semantic reasoner on all types of events: those produced by data-driven and knowledge-driven detectors, as

Figure 5: The dynamic dashboard GUI displaying a shower event. On the right, events are listed as they are detected. The user can also retrieve historical events using the input field at the bottom. The dashboard tab on the left displays an event detected by the unknown pattern detector. well as relabeled and merged events. Each of these tests (We have published these tests at [[https://github.com/idlab-predict/ddashboard-reasoning](https://github.com/idlab-predict/ddashboard-reasoning)]([https://github.com/idlab-predict/ddashboard-reasoning](https://github.com/idlab-predict/ddashboard-reasoning)), accessed on 15 October 2021) show that the reasoning completes within half a second, a delay that will not disturb the user experience. In summary, fully automated event-driven (rather than user-driven) visualization of events using semantic reasoning can be achieved, with reasoning delays well below one second. ### User Feedback Capture for Increased Detector Performance and Automated Labeling

In industry, it is often hard to set up powerful data-driven monitoring systems because large, labeled datasets are seldom available. Therefore, the third research objective set in this paper was to capture and use user feedback in the monitoring system. As labels become available, adaptive detection methods should improve in utility through higher detection accuracy or a wider range of detected relevant events. As described in Section 8, we added functionality in the dynamic dashboard's user interface to label events with meaningful descriptions (event relabeling), to merge events reported by multiple detectors and to reject incorrect event alerts.