The TSQL2 Temporal Query Language
The TSQL2 Temporal Query Language
Generating Network-Based Moving Objects
SSDBM '00 Proceedings of the 12th International Conference on Scientific and Statistical Database Management
Prediction and indexing of moving objects with unknown motion patterns
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Prediction-based monitoring in sensor networks: taking lessons from MPEG
ACM SIGCOMM Computer Communication Review - Special issue on wireless extensions to the internet
Mining Frequent Trajectories of Moving Objects for Location Prediction
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Expert Systems with Applications: An International Journal
Semantics and implementation of continuous sliding window queries over data streams
ACM Transactions on Database Systems (TODS)
A query processor for prediction-based monitoring of data streams
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
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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.