Quality-Aware Sensor Data Stream Management in a Living Lab Environment
Aboubakr Benabbas, Daniela Nicklas · 2019
Sensor data is error-prone. Developers of pervasive applications must take the limitations of sensors into account when processing the data. To relieve the developers from the task of data cleaning and quality monitoring, we need a set of tools to model sensor data quality and to integrate the quality information into the stream data processing. In this dissertation, the goal is to provide a framework of tools to semi-automatically generate sensor models and stream processing queries for sensors with quality and context information for a quality-aware data stream processing.