Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Spatial Data Mining: Database Primitives, Algorithms and Efficient DBMS Support
Data Mining and Knowledge Discovery
A Framework for Generating Network-Based Moving Objects
Geoinformatica
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
Capturing Fuzziness and Uncertainty of Spatiotemporal Objects
ADBIS '01 Proceedings of the 5th East European Conference on Advances in Databases and Information Systems
Real-Time Traffic Updates in Moving Objects Databases
DEXA '02 Proceedings of the 13th International Workshop on Database and Expert Systems Applications
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Knowledge Discovery in Spatial Databases
KI '99 Proceedings of the 23rd Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Time-series prediction with applications to traffic and moving objects databases
Proceedings of the 3rd ACM international workshop on Data engineering for wireless and mobile access
A predictive location model for location-based services
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
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
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling, Storing, and Mining Moving Object Databases
IDEAS '04 Proceedings of the International Database Engineering and Applications Symposium
Practical Data Management Techniques for Vehicle Tracking Data
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Prediction of moving object location based on frequent trajectories
ISCIS'06 Proceedings of the 21st international conference on Computer and Information Sciences
TrajPattern: mining sequential patterns from imprecise trajectories of mobile objects
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Spatio–temporal rule mining: issues and techniques
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
E3TP: A Novel Trajectory Prediction Algorithm in Moving Objects Databases
PAISI '09 Proceedings of the Pacific Asia Workshop on Intelligence and Security Informatics
PutMode: prediction of uncertain trajectories in moving objects databases
Applied Intelligence
Prediction functions in bi-temporal datastreams
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part I
Hotspot district trajectory prediction
WAIM'10 Proceedings of the 2010 international conference on Web-age information management
Human mobility, social ties, and link prediction
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Semantic trajectory mining for location prediction
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Time series case based reasoning for image categorisation
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
Predicting future locations with hidden Markov models
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems
QS-STT: QuadSection clustering and spatial-temporal trajectory model for location prediction
Distributed and Parallel Databases
A “semi-lazy” approach to probabilistic path prediction in dynamic environments
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining sub-trajectory cliques to find frequent routes
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
Proceedings of the VLDB Endowment
Mining geographic-temporal-semantic patterns in trajectories for location prediction
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
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Advances in wireless and mobile technology flood us with amounts of moving object data that preclude all means of manual data processing. The volume of data gathered from position sensors of mobile phones, PDAs, or vehicles, defies human ability to analyze the stream of input data. On the other hand, vast amounts of gathered data hide interesting and valuable knowledge patterns describing the behavior of moving objects. Thus, new algorithms for mining moving object data are required to unearth this knowledge. An important function of the mobile objects management system is the prediction of the unknown location of an object. In this paper we introduce a data mining approach to the problem of predicting the location of a moving object. We mine the database of moving object locations to discover frequent trajectories and movement rules. Then, we match the trajectory of a moving object with the database of movement rules to build a probabilistic model of object location. Experimental evaluation of the proposal reveals prediction accuracy close to 80%. Our original contribution includes the elaboration on the location prediction model, the design of an efficient mining algorithm, introduction of movement rule matching strategies, and a thorough experimental evaluation of the proposed model.