Maintaining knowledge about temporal intervals
Communications of the ACM
Consistency Checking for Qualitative Spatial Reasoning with Cardinal Directions
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach
Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
ST-DBSCAN: An algorithm for clustering spatial-temporal data
Data & Knowledge Engineering
A clustering-based approach for discovering interesting places in trajectories
Proceedings of the 2008 ACM symposium on Applied computing
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Mining GPS data for extracting significant places
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
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This contribution discusses an approach on finding significant locations from a set of GPS tracks, called Hotspots in this contribution. Based on that, a model where traffic infrastructure is represented by a dynamic network of Hotspots is suggested. Besides the location of Hotspots, information about travel times between these Hotspot-Nodes also comes along with the extracted significant places. This information can be used to improve or enrich traffic management and/or navigation systems by consequently achieving a more precise estimation of travel times compared to current systems.