Paper Info
Title
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.
Year
DOI
Venue
2012
10.1007/978-3-642-33486-3_55
ECML/PKDD
Keywords
Field
DocType
spatio-temporal patterns concurrently,object set,extracting trajectory,object data,unifying spatio-temporal pattern mining,versatile analysis,scalable analysis,different kind,real world application,massive scale,spatio-temporal pattern,visualization,trajectories
Data mining,Data set,Visualization,Computer science,Positioning technology,Temporal pattern mining,Vehicle control,Mobile telephony,Scalability
Conference
Citations 
PageRank 
References 
2
0.38
7
Authors
4
Name
Order
Citations
PageRank
Phan Nhat Hai1203.51
Dino Ienco229542.01
Pascal Poncelet3768126.47
Maguelonne Teisseire4557129.00