Probabilistic tracking is a standard technique for estimating the state of moving objects from sensor data. With increase in the numbers of sensors, however, sensor data has become overwhelming. This has led to the usage of database systems for sensor data processing. However, tracking tasks so far have been realized in external components. In this thesis, a solution for integrating tracking methods into generic relational data¬base systems (RDBMS) is provided. For four classes of representative sensor data pro¬cessing algorithms, relational and deductive rules are derived for imple¬menting proba¬bilistic tracking algorithms into RDBMS. These rules are declarative, descriptive formu¬lations for sensor data processing. For neces¬sary additional functionalities concerning probabilistic functions and matrix implementations, several implementation solutions are discussed and evaluated. A new phase concept on data streams enhances the implemen¬tation of anomaly detection. An analysis of the asymptotical runtime be¬havior of the deductive implementation shows its superlinear runtime which is un¬wanted for real-time applications. As a solution, incremental update propagation methods are applied which restores the linear runtime behavior.
The methods are tested on a prototypical system for the detection of criti¬cal situations in airspace monitoring. The test proofs the suitability for real-world scenarios, in this case allowing for processing flight data for all Ger¬man airspace in real time on stand-ard computer hardware.