Hautte Sander Vanden, Moens Pieter, Herwegen Joachim Van, Paepe Dieter De, Steenwinckel Bram, Verstichel Stijn, Ongenae Femke, Hoecke Sofie Van
IDLab, Ghent University - imec, Technologiepark-Zwijnaarde 122, 9052 Gent, Belgium.
Sensors (Basel). 2020 Feb 20;20(4):1152. doi: 10.3390/s20041152.
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.
在工业领域，仪表板常用于监控资产群组，如火车、机器或建筑物。在这类工业资产群组中，大量传感器不断发展，新的传感器数据交换协议和数据格式被引入，可能需要引入新的可视化类型，并且现有的仪表板可视化可能需要根据所显示的传感器进行更新。这些需求推动了动态仪表板应用程序的开发。与固定结构的仪表板应用程序不同，动态仪表板应用程序允许用户随意创建可视化，并且没有硬编码的传感器绑定。动态仪表板的现有技术无法很好地应对必须监控的传感器的频繁添加和移除——这些更改仍必须在实现过程中或由用户在运行时进行配置。此外，用户面对大量的传感器、聚合和可视化可供选择，这有时甚至可能导致创建无意义的仪表板小部件。在本文中，我们展示了一个克服这些问题的动态仪表板。传感器、可视化和聚合可以自动发现，因为它们作为符合Web事物模型的网关之上的RESTful Web事物提供。该网关还提供Web事物的语义注释，描述它们的能力。给定事物注释、逻辑规则和自定义仪表板本体，语义推理器可以得出可视化建议。由此产生的仪表板应用程序会自动呈现可用的传感器、可视化和聚合，无需进行传感器配置，并协助用户构建有意义的仪表板。通过这种方式，用户可以专注于解释传感器数据，并尽早检测和解决操作问题。
