DOMINO: databases fOr MovINg Objects tracking
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
A data model and data structures for moving objects databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A foundation for representing and querying moving objects
ACM Transactions on Database Systems (TODS)
A fine grained heuristic to capture web navigation patterns
ACM SIGKDD Explorations Newsletter
Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
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
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Access Patterns Efficiently from Web Logs
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Mining association rules on significant rare data using relative support
Journal of Systems and Software
Moving objects spatiotemporal reasoning model for battlefield analysis
ICS'08 Proceedings of the 12th WSEAS international conference on Systems
Temporal moving pattern mining for location-based service
Journal of Systems and Software
Techniques for Efficient Road-Network-Based Tracking of Moving Objects
IEEE Transactions on Knowledge and Data Engineering
Mining temporal interval relational rules from temporal data
Journal of Systems and Software
Indexing of Moving Objects on Road Network Using Composite Structure
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Mining frequent trajectory patterns in spatial-temporal databases
Information Sciences: an International Journal
Mining frequent closed patterns in pointset databases
Information Systems
NCO-tree: a spatio-temporal access method for segment-based tracking of moving objects
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
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LBS(Location-Based Service) is generally described as an information service that provides location-based information to its mobile users. Since the conventional studies on data mining do not consider spatial and temporal aspects of data simultaneously, these techniques have limited application in studying the moving objects of LBS with respect to the spatial attributes that is changing over time. In this paper, we propose a new data mining technique and algorithms for identifying temporal patterns from series of locations of moving objects that have temporal and spatial dimensions. For this purpose, we use the spatial operation to generalize a location of moving point, applying time constraints between locations of moving objects to make valid moving sequences. Finally, we show that our technique generates temporal patterns found in frequent moving sequences.