Extracting trajectories through an efficient and unifying spatio-temporal pattern mining system

Phan Nhat Hai, Dino Ienco, Pascal Poncelet, Maguelonne Teisseire

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2012, Proceedings
Pages820-823
Number of pages4
EditionPART 2
DOIs
StatePublished - 2012
Externally publishedYes
Event2012 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2012 - Bristol, United Kingdom
Duration: Sep 24 2012Sep 28 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7524 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2012 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2012
Country/TerritoryUnited Kingdom
CityBristol
Period9/24/129/28/12

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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