Updating and Querying Databases that Track Mobile Units
Distributed and Parallel Databases - Special issue on mobile data management and applications
A Comparison of Protocols for Updating Location Information
Cluster Computing
Novel Approaches in Query Processing for Moving Object Trajectories
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Techniques for Efficient Road-Network-Based Tracking of Moving Objects
IEEE Transactions on Knowledge and Data Engineering
On-line data reduction and the quality of history in moving objects databases
MobiDE '06 Proceedings of the 5th ACM international workshop on Data engineering for wireless and mobile access
Spatio-temporal data reduction with deterministic error bounds
The VLDB Journal — The International Journal on Very Large Data Bases
On a generic uncertainty model for position information
QuaCon'09 Proceedings of the 1st international conference on Quality of context
Usability analysis of compression algorithms for position data streams
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Efficient real-time trajectory tracking
The VLDB Journal — The International Journal on Very Large Data Bases
Context-aware and quality-aware algorithms for efficient mobile object management
Pervasive and Mobile Computing
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Moving objects databases (MODs) store objects' trajectories by spatiotemporal polylines that approximate the actual movements given by sequences of sensed positions. Determining such a polyline with as few vertices as possible under the constraint that it does not deviate by more than a certain accuracy bound ε from the sensed positions is an algorithmic problem known as trajectory reduction. A specific challenge is online trajectory reduction, i.e. continuous reduction with position sensing in realtime. This particularly is required for moving objects with embedded position sensors whose movements are tracked and stored by a remote MOD. In this paper, we present Connection-preserving Dead Reckoning (CDR), a new approach for online trajectory reduction. It outperforms the existing approaches by 30 to 50%. CDR requires the moving objects to temporally store some of the previously sensed positions. Although the storage consumption of CDR generally is small, it is not bounded. We therefore further present CDRM whose storage allocation and execution time per position fix can be adjusted and limited. Even with very limited storage allocations of less than 1 kB CDRM outperforms the existing approach by 20 to 40%.