The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Finding patterns in time series: a dynamic programming approach
Advances in knowledge discovery and data mining
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Novel Approaches in Query Processing for Moving Object Trajectories
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Indexing the Distance: An Efficient Method to KNN Processing
Proceedings of the 27th International Conference on Very Large Data Bases
Time-series similarity problems and well-separated geometric sets
Nordic Journal of Computing
Indexing spatio-temporal trajectories with Chebyshev polynomials
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Indexing the Trajectories of Moving Objects in Networks*
Geoinformatica
Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
TMN-tree: New Trajectory Index Structure for Moving Objects in Spatial Networks
CIT '10 Proceedings of the 2010 10th IEEE International Conference on Computer and Information Technology
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Searching similar trajectories in real time has been a challenging task in a large variety of location-aware applications. This paper addresses its two key issues, i.e. evaluating the similarity between two trajectories reasonably and effectively, and providing efficient algorithms to support queries in real time. Firstly, a novel similarity measurement, called Global Temporal Similarity (GTS), is suggested, which is perturbation-free and effective since it takes into account both the evolution of the similarity over time and the spatial movements. Secondly, a new index structure with linear updated time, called Real Time Similar Trajectory Searching-tree (RTSTS-tree), is proposed to support the search of similar trajectories. Besides, to support k Nearest Neighbor (kNN) query of trajectories and Top k Similar Pairs query, two algorithms are proposed based on GTS and RTSTS-tree and are capable of searching similar trajectories by object and by location with the time complexity of O(n) and O(n2) respectively. Finally, the results of the extensive experiments conducted on real and synthetic data set validate the effectiveness and the efficiency of the proposed similarity measurement, index structure and query algorithms.