Extracting Trajectories Through an Efficient and Unifying Spatio-Temporal Pattern Mining System
Abstract
Recent improvements in positioning technology has led to a much wider availability of massive moving object data. A crucial task is to find the moving objects that travel together. Usually, these object sets are called spatio-temporal patterns. Analyzing such data has been applied in many real world applications, e.g., in ecological study, vehicle control, mobile communication management, etc. However, few tools are available for flexible and scalable analysis of massive scale moving objects. Additionally, there is no framework devoted to efficiently manage multiple kinds of patterns at the same time. Motivated by this issue, we propose a framework, named GeT_Move , which is designed to extract and manage different kinds of spatio-temporal patterns concurrently. A user-friendly interface is provided to facilitate interactive exploration of mining results. Since GeT_Move is tested on many kinds of real data sets, it will benefit users to carry out versatile analysis on these kinds of data by exhibiting different kinds of patterns efficiently.
Cite
Text
Hai et al. "Extracting Trajectories Through an Efficient and Unifying Spatio-Temporal Pattern Mining System." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012. doi:10.1007/978-3-642-33486-3_55Markdown
[Hai et al. "Extracting Trajectories Through an Efficient and Unifying Spatio-Temporal Pattern Mining System." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012.](https://mlanthology.org/ecmlpkdd/2012/hai2012ecmlpkdd-extracting/) doi:10.1007/978-3-642-33486-3_55BibTeX
@inproceedings{hai2012ecmlpkdd-extracting,
title = {{Extracting Trajectories Through an Efficient and Unifying Spatio-Temporal Pattern Mining System}},
author = {Hai, Phan Nhat and Ienco, Dino and Poncelet, Pascal and Teisseire, Maguelonne},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2012},
pages = {820-823},
doi = {10.1007/978-3-642-33486-3_55},
url = {https://mlanthology.org/ecmlpkdd/2012/hai2012ecmlpkdd-extracting/}
}