TY - GEN
T1 - Extracting trajectories through an efficient and unifying spatio-temporal pattern mining system
AU - Hai, Phan Nhat
AU - Ienco, Dino
AU - Poncelet, Pascal
AU - Teisseire, Maguelonne
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84866858647&partnerID=8YFLogxK
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U2 - 10.1007/978-3-642-33486-3_55
DO - 10.1007/978-3-642-33486-3_55
M3 - Conference contribution
AN - SCOPUS:84866858647
SN - 9783642334856
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 820
EP - 823
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2012, Proceedings
T2 - 2012 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2012
Y2 - 24 September 2012 through 28 September 2012
ER -