## 7 Automated Event Visualization Using Semantic Reasoning
### Summary

In summary, all research objectives for this paper were attained. We explained the role of different components in our architecture and explained how events are exchanged

Figure 6: Bathroom humidity over a period of 21 days. Red fragments (“A”) indicate events detected by the unknown pattern detector from Section 5. After a user confirms events A0 and A4, seventeen similar segments are found by the known pattern detector (orange, “F”). This mechanism can greatly reduce labeling efforts. and stored persistently. The system enables detection and alerting of events in real-time measurement data at high volume and velocity, as evaluated on the ventilation monitoring use case. Semantic reasoning avoids the need for human intervention when visualizing events. As reasoning time stays well below one second, a smooth user experience is ensured. Through user interaction with the dashboard, limited amounts of human feedback enable automatic methods to label a large amount of new events automatically. Finally, other industrial use cases can easily reuse the dynamic dashboard for event visualization and feedback capturing, the unified event data format, and the architecture for exchange of event information. ## 11 Conclusions

In industry, the lack of available large, labeled datasets is often a problem for the roll-out of accurate, remote asset monitoring systems. Moreover, such monitoring systems traditionally employ dashboards that overview the state of assets in the field, these require a lot of manual effort from the operator to visualize important events occurring in the data. In this paper, we present an approach to detect events in sensor data, with no initial labels available and requiring only limited intervention from system experts, applied to a ventilation monitoring use case. Our approach consists of a dynamic dashboard that displays both measurement data and detected events within these measurements. Events originate from both data-driven and knowledge-driven detector components and are described in a semantic format. When a user wishes to inspect an event, semantic reasoning is used to automatically determine suitable widgets to be displayed, reducing the workload for the user. By interacting with the dashboard, the user provides feedback by describing events or providing a ground truth regarding the events. As soon as feedback is available, it is fed back to the dynamic detectors. Even with a limited amount of feedback, these dynamic detectors can automatically detect and label a large amount of future events. As such, this system addresses many of the roadblocks faced in industry. Next, we present some suggestions for future work. A dashboard user will likely not deem all visualizations as an interesting event. For instance, an event detector may report that a combination of three sensor properties was important in its decision for flagging a pattern as unexpected, but an expert may immediately see that only one of the three sensor properties actually shows abnormal behavior. In such cases, the user may delete irrelevant widgets from the dashboard, or add additional widgets. Such dashboard interactions can be used to improve the automatically generated visualizations for similar events. Ideally, the dashboard is able to tailor the visualizations to the specific context in which the event occurred (e.g., the asset or operating conditions), or to personalize the dashboard for the user. To this end, the semantic reasoning approach may be fused with machine learning-based recommendation engines to further optimize and tailor the visualisation suggestions. Besides more advanced dashboard interactions, the scalability of the whole system should also be evaluated to ensure a large amount of unknown events can be defined and visualized to the end user. While the currently implemented system already uses the Obelisk stack through a consumer-producer paradigm [3], a clear evaluation investigating the load and the possible user burden of such a system is still needed.