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
Computing longest duration flocks in trajectory data
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
Continuous Clustering of Moving Objects
IEEE Transactions on Knowledge and Data Engineering
Discovery of convoys in trajectory databases
Proceedings of the VLDB Endowment
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 have led to a 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. Additionally, we have to execute these algorithms again whenever new data are added to the existing database. To address these issues, we first redefine spatio-temporal 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 results show that our approaches are very effective and outperform existing algorithms in terms of efficiency.