Mining, indexing, and querying historical spatiotemporal data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient mining of group patterns from user movement data
Data & Knowledge Engineering
Discovery of convoys in trajectory databases
Proceedings of the VLDB Endowment
MoveMine: mining moving object databases
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Swarm: mining relaxed temporal moving object clusters
Proceedings of the VLDB Endowment
Mining moving object, trajectory and traffic data
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
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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.