Skip to Main content Skip to Navigation
Journal articles

Get_Move : fouille de données d’objets mobiles

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, they are called spatio-temporal patterns. Due to the emergence of many different kinds of spatio-temporal patterns in recent years, different approaches have been proposed to extract them. However, each approach only focuses on mining a specific kind of pattern. In addition to the fact that it is a painstaking task due to the large number of algorithms used to mine and manage patterns, it is also time consuming. To address these issues, we first redefine spatiotemporal patterns in the itemset context. Secondly, we propose a unifying approach, named GeT_Move, using a frequent closed itemset-based spatio-temporal pattern-mining algorithm to mine and manage different spatio-temporal patterns. GeT_Move is implemented in two versions which are GeT_Move and Incremental GeT_Move. Experiments are performed on real and synthetic datasets and the experimental results show that our approaches are very effective and outperform existing algorithms in terms of efficiency.
Document type :
Journal articles
Complete list of metadata
Contributor : Migration Irstea Publications <>
Submitted on : Friday, December 11, 2020 - 11:00:31 AM
Last modification on : Tuesday, September 7, 2021 - 3:55:02 PM


Files produced by the author(s)



Nhat Hai Phan, Pascal Poncelet, Maguelonne Teisseire. Get_Move : fouille de données d’objets mobiles. Ingéniérie des Systèmes d'Information, Lavoisier, 2013, 18 (4), pp.145-169. ⟨10.3166/ISI.18.4.145-169⟩. ⟨hal-02601105⟩



Record views


Files downloads