Processing real-time sensor data streams for 3D web visualization

Today, myriads of sensors are surrounding us. Their usage ranges from environmental monitoring (e.g., weather and air quality), over sensor-equipped smart buildings, to the quantified self and other human observing applications. The data streams produced by such sensors often update with high frequencies, resulting in large data volumes. Being able to analyze those real-time sensor data streams requires efficient visualization techniques. In our work, we explore how 3D visualizations can be used to extend the available information space. More specifically, we present an approach for processing real-time sensor data streams to enable scalable Web-based 3D visualizations. Based on an event-driven architecture, our key contribution is the presentation of three processing patterns to optimize transmission of sensor data streams to 3D Web clients.