Moving Object Stream Processing With Short-Time Prediction

Spatio-temporal data from Moving Objects is often available as a live data stream and needs to be processed accordingly. The trajectories update while the objects are moving. Even though the data arrives in a streaming manner, significant delays between location updates are possible, resulting in delayed or less accurate results of continuous queries on the trajectories. That can be an important issue when using the queries for real-world decisions, e. g., with Automatic Identification System (AIS) data for maritime navigation. Additionally, short-time predictions can be useful to get early warnings for critical situations. Previous work does not cover this problem for streaming applications, as existing systems are mainly Moving Object Databases, which are not optimized for streaming data. In this work, we describe how spatio-temporal inter and extrapolation can be integrated into Data Stream Management Systems and which challenges have to be solved doing so.