Relevance feedback retrieval of time series data
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
On the approximation of curves by line segments using dynamic programming
Communications of the ACM
Knowledge-Based Event Detection in Complex Time Series Data
AIMDM '99 Proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making
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
Sampling Trajectory Streams with Spatiotemporal Criteria
SSDBM '06 Proceedings of the 18th International Conference on Scientific and Statistical Database Management
Trajectory Compression under Network Constraints
SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
Semantic Trajectory Compression
SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
Algorithms for compressing GPS trajectory data: an empirical evaluation
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
SQUISH: an online approach for GPS trajectory compression
Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications
Compression of GPS Trajectories
DCC '12 Proceedings of the 2012 Data Compression Conference
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The increasing of location aware mobile devices such as vehicle navigation equipment and smart phones has enabled the collection of massive trajectories data. Movement trajectory compression has become an urgent necessity to store these data. Traditional algorithms for trajectory compression are based on the location distribution of sampling points, and often lead to intolerable error with a high compression rate. In urban road network, the movements of vehicles are usually bounded by road network. An initial thought of how to make use of semantics in trajectory compression is to represent the compressed trajectory in road segments with the entry time and the leaving time information attached. However, the movement of moving object during the road is completely abandoned. This paper has proposed an algorithm named enhanced semantic trajectory compression (EHSTC) that compress trajectories based on road semantics as well as motion feature. During chunking sampling points in a road segment, those points with great motion feature changes will be detected and stored in the feature point list of underlying road segment. The experimental result on real trajectories demonstrates the effectiveness and efficiency of the proposed solution.