Prediction Functions in Bi-temporal Datastreams

Modern datastream management system (DSMS) assume sensor measurements to be constant valued until an update is measured. They do not consider continuously changing measurement values, although a lot of real world scenarios exist that need this essential property. For instance, modern cars use sensors, like radar, to periodically detect dynamic objects like other vehicles. The state of these objects (position and bearing) changes continuously, so that it must be predicted between two measurements. Therefore, in our work we develop a new bitemporal stream algebra for processing continuously changing stream data. One temporal dimension covers correct order of stream elements and the other covers continuously changing measurements. Our approach guarantees deterministic query results and correct optimizability. Our implementation shows that prediction functions can be processed very efficiently.