With the increasing use of sensor technology, the compression of sensor data streams is getting more and more important to reduce both the costs of further processing as well as the data volume for persistent storage. A popular method for sensor data compression is to smooth the original measurement curve by an approximated curve, which is bounded by a given maximum error value.

Measurement values from positioning systems like GPS are an interesting special case, because they consist of two spatial and one temporal dimension. Therefore various standard techniques for approximation calculations like regression or line simplification algorithms cannot be directly applied.

In this paper, we portray our stream data management system NexusDS and an operator for compressing sensor data. For the operator, we implemented various compression algorithms for position data streams. We present the required adaptations and the different characteristics of the compression algorithms as well as the results of our evaluation experiments, and compare them with a map matching approach, specifically developed for position data.