The computer music tutorial
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A Framework for Generating Network-Based Moving Objects
Geoinformatica
The Geometry of Uncertainty in Moving Objects Databases
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Prediction and indexing of moving objects with unknown motion patterns
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Mining, indexing, and querying historical spatiotemporal data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Managing uncertainty in moving objects databases
ACM Transactions on Database Systems (TODS)
CrimeNet explorer: a framework for criminal network knowledge discovery
ACM Transactions on Information Systems (TOIS)
Criminal network analysis and visualization
Communications of the ACM - 3d hard copy
Mining sequences with temporal annotations
Proceedings of the 2006 ACM symposium on Applied computing
Query languages for moving object databases
Query languages for moving object databases
Enhancing border security: Mutual information analysis to identify suspect vehicles
Decision Support Systems
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Frequent Trajectories of Moving Objects for Location Prediction
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Extending continuous time Bayesian networks
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Visualizing criminal relationships: comparison of a hyperbolic tree and a hierarchical list
Decision Support Systems
Process-driven collaboration support for intra-agency crime analysis
Decision Support Systems - Special issue: Intelligence and security informatics
Continuous time bayesian networks
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Learning continuous time bayesian networks
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
VCCM mining: mining virtual community core members based on gene expression programming
WISI'06 Proceedings of the 2006 international conference on Intelligence and Security Informatics
Managing uncertain spatio-temporal data
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Querying and Mining Uncertain Spatio-Temporal Data
Efficient fuzzy ranking queries in uncertain databases
Applied Intelligence
Predictive spatio-temporal queries: a comprehensive survey and future directions
Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems
DART: an efficient method for direction-aware bichromatic reverse k nearest neighbor queries
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
Stream mining on univariate uncertain data
Applied Intelligence
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Objective: Prediction of moving objects with uncertain motion patterns is emerging rapidly as a new exciting paradigm and is important for law enforcement applications such as criminal tracking analysis. However, existing algorithms for prediction in spatio-temporal databases focus on discovering frequent trajectory patterns from historical data. Moreover, these methods overlook the effect of some important factors, such as speed and moving direction. This lacks generality as moving objects may follow dynamic motion patterns in real life. Methods: We propose a framework for predicating uncertain trajectories in moving objects databases. Based on Continuous Time Bayesian Networks (CTBNs), we develop a trajectory prediction algorithm, called PutMode (Prediction of uncertain trajectories in Moving objects databases). It comprises three phases: (i) construction of TCTBNs (Trajectory CTBNs) which obey the Markov property and consist of states combined by three important variables including street identifier, speed, and direction; (ii) trajectory clustering for clearing up outlying trajectories; (iii) predicting the motion behaviors of moving objects in order to obtain the possible trajectories based on TCTBNs. Results: Experimental results show that PutMode can predict the possible motion curves of objects in an accurate and efficient manner in distinct trajectory data sets with an average accuracy higher than 80%. Furthermore, we illustrate the crucial role of trajectory clustering, which provides benefits on prediction time as well as prediction accuracy.