A Dynamic Dashboarding Application for Fleet Monitoring Using Semantic Web of Things Technologies

Abstract

In industry, dashboards are often used to monitor fleets of assets, such as trains, machines or buildings. In such industrial fleets, the vast amount of sensors evolves continuously, new sensor data exchange protocols and data formats are introduced, new visualization types may need to be introduced and existing dashboard visualizations may need to be updated in terms of displayed sensors. These requirements motivate the development of dynamic dashboarding applications. These, as opposed to fixed-structure dashboard applications, allow users to create visualizations at will and do not have hard-coded sensor bindings. The state-of-the-art in dynamic dashboarding does not cope well with the frequent additions and removals of sensors that must be monitored—these changes must still be configured in the implementation or at runtime by a user. Also, the user is presented with an overload of sensors, aggregations and visualizations to select from, which may sometimes even lead to the creation of dashboard widgets that do not make sense. In this paper, we present a dynamic dashboard that overcomes these problems. Sensors, visualizations and aggregations can be discovered automatically, since they are provided as RESTful Web Things on a Web Thing Model compliant gateway. The gateway also provides semantic annotations of the Web Things, describing what their abilities are. A semantic reasoner can derive visualization suggestions, given the Thing annotations, logic rules and a custom dashboard ontology. The resulting dashboarding application automatically presents the available sensors, visualizations and aggregations that can be used, without requiring sensor configuration, and assists the user in building dashboards that make sense. This way, the user can concentrate on interpreting the sensor data and detecting and solving operational problems early.

Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications

“…This manual configuration is required, on the one hand, at design time to instruct how to fetch data and bind it correctly to the available data processing and visualization components, and at runtime, on the other hand, there is still the burden for the user to select from a plethora of available sensors, data processing components and visualizations. In previous research [26], we proposed to wrap sensors, data processing components and visualizations as Web Things, i.e., APIs, that can be automatically discovered, called and combined, and this way to reduce the sensor and visualization choice overload by reasoning over the available metadata that the Web Things are annotated with. Within the Living Lab Smart Maintenance, this dynamic dashboard using semantic reasoning is integrated in the enterprise tier, on top of Obelisk.…”

“…These enterprise tier dashboard applications combine data from different sources, queried from the platform tier, and present it to the user in an intuitive manner. For this living lab, the dynamic dashboard application developed by IDLab [26] enables end-users, e.g., operators, managers, to query the most relevant data from the information services, e.g., physical assets, and machine learning algorithms, by querying Obelisk and aggregating the data into meaningful visualizations accessible through a dynamic Graphical User Interface (GUI). The purpose of the dashboard is to ensure that the end-user can make business decisions in a timely manner based on the data.…”

“…However, enterprise tier applications, such as the dashboard application, can be deployed outside of the edge tier networks. As described in more detail in [26], the most pragmatic solution for discovery of sensors within a remote network is by providing a root address for each sensor network at run-time. Then, upon HTTP GET request to that IP address and a predetermined TCP port, a discovery service responds with a list of the available services and how to reach them.…”

“…As pointed out earlier, not only sensors are presented as Web Things, also the visualizations used in the dashboard GUI are retrieved this way and they too are semantically annotated. By doing so, the dashboard broker is capable of semantic reasoning to match sensor properties to valid visualizations using the EYE server [26]. This partially automates the process of dashboard creation and assists the end-user in picking the most relevant visualization from the high number of supported visualization types.…”

A Meta-Model Integration for Supporting Knowledge Discovery in Specific Domains: A Case Study in Healthcare

“…In this context, dashboards are one of the most useful tools for generating knowledge about different data domains. Also, dashboards are popular solutions to exploit the data provided by sensors embedded in the environment, intelligent devices and the IoT [42,43].…”