On-line discovery of hot motion paths
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
A sensor network for compression and streaming of GPS trajectory data
Proceedings of the 6th ACM conference on Embedded network sensor systems
Monitoring continuous queries over streaming locations
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Monitoring Orientation of Moving Objects around Focal Points
SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
Spatiotemporal sampling for trajectory streams
Proceedings of the 2010 ACM Symposium on Applied Computing
On the effect of trajectory compression in spatiotemporal querying
ADBIS'07 Proceedings of the 11th East European conference on Advances in databases and information systems
Online amnesic summarization of streaming locations
SSTD'07 Proceedings of the 10th international conference 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
EWSN'11 Proceedings of the 8th European conference on Wireless sensor networks
SQUISH: an online approach for GPS trajectory compression
Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications
SeTraStream: semantic-aware trajectory construction over streaming movement data
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
Efficient real-time trajectory tracking
The VLDB Journal — The International Journal on Very Large Data Bases
GeoSearch: georeferenced video retrieval system
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
A two-layer approach for energy efficiency in mobile location sensing applications
IFIP'12 Proceedings of the 11th international IFIP TC 6 conference on Networking - Volume Part II
Machine learning for vessel trajectories using compression, alignments and domain knowledge
Expert Systems with Applications: An International Journal
Multiplexing trajectories of moving objects
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Map-matched trajectory compression
Journal of Systems and Software
Semantic trajectories modeling and analysis
ACM Computing Surveys (CSUR)
TrajMetrix: a trajectory compression benchmarking framework
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
EHSTC: an enhanced method for semantic trajectory compression
Proceedings of the 4th ACM SIGSPATIAL International Workshop on GeoStreaming
Direction-preserving trajectory simplification
Proceedings of the VLDB Endowment
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Monitoring movement of high-dimensional points is essential for environmental databases, geospatial applications, and biodiversity informatics as it reveals crucial information about data evolution, provenance detection, pattern matching etc. Despite recent research interest on processing continuous queries in the context of spatiotemporal data streams, the main focus is on managing the current location of numerous moving objects. In this paper, we turn our attention onto a historical perspective of movement and examine trajectories generated by streaming positional updates. The key challenge is how to maintain a concise, yet quite reliable summary of each object's movement, avoiding any superfluous details and saving in processing complexity and communication cost. We propose two single-pass approximation techniques based on sampling that take advantage of the spatial locality and temporal timeliness inherent in trajectory streams. As a means of reducing substantially the scale of the datasets, we utilize heuristic prediction to distinguish which locations to preserve in the compressed trajectories. A comprehensive experimental study verifies the stability and robustness of the proposed techniques and demonstrates that intelligent compression schemes are able to act as effective load shedding operators achieving remarkable results.