FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Research issues in data stream association rule mining
ACM SIGMOD Record
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Using Data Mining to Estimate Missing Sensor Data
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
A Framework for Mining Sequential Patterns from Spatio-Temporal Event Data Sets
IEEE Transactions on Knowledge and Data Engineering
Understanding mobility based on GPS data
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Mining interesting locations and travel sequences from GPS trajectories
Proceedings of the 18th international conference on World wide web
WhereNext: a location predictor on trajectory pattern mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Using data mining to handle missing data in multi-hop sensor network applications
Proceedings of the Ninth ACM International Workshop on Data Engineering for Wireless and Mobile Access
Mind the gap: large-scale frequent sequence mining
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Mining frequent trajectory pattern based on vague space partition
Knowledge-Based Systems
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The rapid development and deployment of location-acquisition equipment such as GPS systems and GSM communication networks has made collection of spatio-temporal trajectory datasets possible and led to the demand of managing and mining patterns from trajectory datasets to discover objects' movement behavior. As trajectories are generated continuously without limitation and boundaries, they form stream data. Though there are lots of research work done on mining trajectory datasets, none of them considers trajectory data as streams. They treat trajectory data as static data and run multiple scans on the data. In this paper, we present our efforts in facilitating this demand by developing a novel stream data mining algorithm to discover spatio-temporal sequential patterns from trajectories in real time; our algorithm is the first on-line trajectory mining algorithm and only needs to scan the trajectory dataset one time. We also propose a new data structure, called trajectory stream mining tree (TSM-tree), to store and represent up-to-date trajectory patterns. We conduct experiments using real life trajectory datasets to evaluate the performance of our algorithm.