Updating and Querying Databases that Track Mobile Units
Distributed and Parallel Databases - Special issue on mobile data management and applications
Statistical Language Learning
Realm-based spatial data types: the ROSE algebra
The VLDB Journal — The International Journal on Very Large Data Bases
Cost and Imprecision in Modeling the Position of Moving Objects
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Novel Approaches in Query Processing for Moving Object Trajectories
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Capturing the Uncertainty of Moving-Object Representations
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Indexing spatio-temporal trajectories with Chebyshev polynomials
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Mining, indexing, and querying historical spatiotemporal data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Managing uncertainty in moving objects databases
ACM Transactions on Database Systems (TODS)
Querying Imprecise Data in Moving Object Environments
IEEE Transactions on Knowledge and Data Engineering
Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Indexing Spatio-Temporal Trajectories with Efficient Polynomial Approximations
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
On the marriage of Lp-norms and edit distance
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Adaptive fastest path computation on a road network: a traffic mining approach
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
On-line discovery of hot motion paths
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Discovery of convoys in trajectory databases
Proceedings of the VLDB Endowment
OpenStreetMap: User-Generated Street Maps
IEEE Pervasive Computing
Continuous probabilistic nearest-neighbor queries for uncertain trajectories
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Mining interesting locations and travel sequences from GPS trajectories
Proceedings of the 18th international conference on World wide web
Convoy Queries in Spatio-Temporal Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Effectively indexing uncertain moving objects for predictive queries
Proceedings of the VLDB Endowment
Traffic density-based discovery of hot routes in road networks
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
Swarm: mining relaxed temporal moving object clusters
Proceedings of the VLDB Endowment
Probabilistic range queries for uncertain trajectories on road networks
Proceedings of the 14th International Conference on Extending Database Technology
Discovering popular routes from trajectories
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Nearest neighbor search on moving object trajectories
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Reducing Uncertainty of Low-Sampling-Rate Trajectories
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Data centric research at the University of Queensland
ACM SIGMOD Record
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Due to the prevalence of GPS-enabled devices and wireless communications technologies, spatial trajectories that describe the movement history of moving objects are being generated and accumulated at an unprecedented pace. Trajectory data in a database are intrinsically heterogeneous, as they represent discrete approximations of original continuous paths derived using different sampling strategies and different sampling rates. Such heterogeneity can have a negative impact on the effectiveness of trajectory similarity measures, which are the basis of many crucial trajectory processing tasks. In this paper, we pioneer a systematic approach to trajectory calibration that is a process to transform a heterogeneous trajectory dataset to one with (almost) unified sampling strategies. Specifically, we propose an anchor-based calibration system that aligns trajectories to a set of anchor points, which are fixed locations independent of trajectory data. After examining four different types of anchor points for the purpose of building a stable reference system, we propose a geometry-based calibration approach that considers the spatial relationship between anchor points and trajectories. Then a more advanced model-based calibration method is presented, which exploits the power of machine learning techniques to train inference models from historical trajectory data to improve calibration effectiveness. Finally, we conduct extensive experiments using real trajectory datasets to demonstrate the effectiveness and efficiency of the proposed calibration system.