## 7 Automated Event Visualization Using Semantic Reasoning
The events being mined consist of all user confirmed events, as well as several meaningful generic indicators such as the time of day (per hour), the weekday or season. This allows the component to find rules such as \"windows are often opened at 8 AM during summer\". While the component does not detect events itself, the rules can be used to help the user understand specific types of events (e.g., event X only occurs in winter), or to suggest likely event descriptions to events being relabeled by the user. As the component periodically mines for rules using the confirmed events, rules may become replaced by others as the user provides more input. ### Semantic Rule Mining

The unified semantic format of events described in this paper also allows for semantic reasoning-based rule mining techniques. All detected events are continuously added into a single Knowledge Graph (KG), stored in a semantic database. From this KG, a semantic rule miner [4] infers cause-effect rules that predict events. These rules are inferred using the supervised framework Descriptive Logic Learner (DL-Learner) [31]. They must predict, with a configured minimum confidence, which occurrences of observations, sensors and system contexts typically lead to new events. Design details of this semantic rule miner are documented in [PERSON] et al. [4]. The mined rules are similar to the expert rules used by the semantic fault detector, but they are now gathered automatically. Moreover, as operators relabel, merge or reject events in the dashboard, the corresponding event knowledge in the semantic database is constantly evolving. So, semantic rule mining allows adapting the detection systemcontinuously and automatically, rather than having to rely on the constant presence of system experts to manually provide and adapt system logic. On the ventilation unit monitoring data, this detection technique is able to learn rules to detect and label new shower echo effects. ## 10 Implementation and Evaluation

This section will overview a system architecture that integrates all discussed event detectors and the dynamic dashboard and will evaluate the results obtained on the real-world, streaming data of the ventilation unit monitoring use case that was presented in Section 3. ### A Monitoring Architecture for Real-World, Streaming Data

The first research objective set in this paper was building a complete architecture for monitoring real-world home ventilation units that upload sensor data continuously. To this end, we have built an architecture that combines all components presented in the previous sections and guarantees that all event detectors can cope with the volume and velocity of streaming sensor data uploaded in real time by many ventilation units. The dynamic dashboarding platform must display event alerts as soon as they have been detected. Feedback from dashboard users about the correctness of detected events must rapidly be exchanged with adaptive event detectors. A unified, semantic event data model was already proposed to exchange event information between the system components. However, the system also needs a solution for fast exchange of event information and for permanent storage of event information, to enable later retrieval in bulk by the dashboard, e.g., when a user wants to list events detected over the past weekend. Figure 4 provides an overview of our system architecture and shows how event information is passed between all system components. Data-driven detectors are displayed in blue, knowledge-driven detectors in green. Arrows indicate the flow direction of information, often a detected event or an update of event information, e.g., an update based on user feedback (displayed at the top right of the figure). Where this work focuses on the dashboard architecture, the work by [PERSON] et al. [17] discusses the overall system architecture of our use case, including sensor data ingest, deployment and semantification.