A system for discovering regions of interest from trajectory data
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
Inferring User Context from Spatio-Temporal Pattern Mining for Mobile Application Services
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Semantic trajectories modeling and analysis
ACM Computing Surveys (CSUR)
A Hybrid Grid-based Method for Mining Arbitrary Regions-of-Interest from Trajectories
Proceedings of Workshop on Machine Learning for Sensory Data Analysis
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We show how to find regions of interest (ROIs) in trajectory databases. ROIs are regions where a large number of moving objects remain for at least a given time interval. Previous techniques use somewhat restrictive definitions for ROIs, and are parameter-dependent. They require sequential scanning of the entire dataset to find ROIs when the ROI parameters change. Our approach is parameter independent, so that the user can quickly identify ROIs under different parametric definitions without rescanning the whole database. We also generalize ROIs to be regions of arbitrary shape of some predefined density. We have tested our methods with large real and synthetic datasets to test the scalability and verify the output of our methods. Our methods give meaningful output and scale very well.