Nowadays, the Internet of Things (IoT) and mobile devices are ubiquitous. Both, the IoT and mobile devices contain sensors and thus can provide data about the device’s environment. The sensor data can be used to infer the current context. However, for this purpose, the sensor data have to be aggregated. In this aggregation process, several different sensors and data provided by other sources, such as databases, can be used. In order to facilitate this, the paper presents a modeling language for context modeling based on sensors. Moreover, a detailed usability evaluation of the presented context modeling language is shown. This evaluation is based on three hypotheses regarding learnability, time expenditure and effectiveness. An experiment involving an experimental group and a control group was conducted to test these three hypotheses, and the results were interpreted.

如今，物联网（IoT）和移动设备无处不在。物联网和移动设备都包含传感器，因此可以提供有关设备环境的数据。传感器数据可用于推断当前上下文。但是，为此，必须汇总传感器数据。在此聚合过程中，可以使用其他来源（例如数据库）提供的几种不同的传感器和数据。为了促进这一点，本文提出了一种用于基于传感器的上下文建模的建模语言。此外，显示了所提供的上下文建模语言的详细可用性评估。该评估基于关于可学习性，时间花费和有效性的三个假设。进行了一个由实验组和对照组组成的实验，以检验这三个假设，