A Framework for Generating Network-Based Moving Objects
Geoinformatica
Mining Frequent Spatio-Temporal Sequential Patterns
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A model for enriching trajectories with semantic geographical information
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
A clustering-based approach for discovering interesting places in trajectories
Proceedings of the 2008 ACM symposium on Applied computing
Building real-world trajectory warehouses
Proceedings of the Seventh ACM International Workshop on Data Engineering for Wireless and Mobile Access
Adding semantic modules to improve goal-oriented analysis of data warehouses using I-star
Journal of Systems and Software
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In the paper we present algorithms for determining regions of interest in the context of movement patterns. The developed algorithms are based on the clustering of grid cells. The grid cells are merged on the basis of the information about movement flows between cells and the number of trajectories that intersected them. The proposed solutions allow to determine the rectangular regions of interest of different size. The size of a resulting region depends on the intensity of movement flows. To determine flows between regions the interpolation of regions has been applied. The interpolation of regions uses a linear interpolation function at the output of which we get the intersection points between the trajectory segment and grid cells. This paper also shortly reviews existing approaches to constructing regions of interest.