Updating and Querying Databases that Track Mobile Units
Distributed and Parallel Databases - Special issue on mobile data management and applications
Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Infominer: mining surprising periodic patterns
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining long sequential patterns in a noisy environment
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
IEEE Transactions on Knowledge and Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
K-Nearest Neighbor Search for Moving Query Point
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Adaptive stream resource management using Kalman Filters
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Prediction and indexing of moving objects with unknown motion patterns
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
STRIPES: an efficient index for predicted trajectories
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
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
Trajectories Mining for Traffic Condition Renewing
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
A Framework for Trajectory Clustering
GSN '09 Proceedings of the 3rd International Conference on GeoSensor Networks
Trajectory pattern analysis for urban traffic
Proceedings of the Second International Workshop on Computational Transportation Science
Trajectory Clustering via Effective Partitioning
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
A framework of mining semantic regions from trajectories
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Pattern-based moving object tracking
Proceedings of the 2011 international workshop on Trajectory data mining and analysis
Prediction of moving object location based on frequent trajectories
ISCIS'06 Proceedings of the 21st international conference on Computer and Information Sciences
Discovering hot topics from geo-tagged video
Neurocomputing
Effectively grouping trajectory streams
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
Dealing with trajectory streams by clustering and mathematical transforms
Journal of Intelligent Information Systems
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Mobile objects have become ubiquitous in our everyday lives, ranging from cellular phones to sensors, therefore, analyzing and mining mobile data becomes an interesting problem with great practical importance. For instance, by finding trajectory patterns of the mobile clients, the mobile communication network can allocate resources more efficiently. However, due to the limited power of the mobile devices, we are only able to obtain the imprecise location of a mobile object at a given time. Sequential patterns are a popular data mining model. By applying the sequential pattern model on the set of imprecise trajectories of the mobile objects, we may uncover important information or further our understanding of the inherent characteristics of the mobile objects, e.g., constructing a classifier based on the discovered patterns or using the patterns to improve the accuracy of location prediction. Since the input data is highly imprecise, it may not be possible to directly apply any existing sequential pattern discovery algorithm to the problem in this paper. Thus, we propose the model of the trajectory patterns and a novel measure to represent the expected occurrences of a pattern in a set of imprecise trajectories. The concept of pattern groups is introduced to present the trajectory patterns in a concise manner. Since the Apriori property no longer holds on the trajectory patterns, a new min-max property is identified and a novel TrajPattern algorithm is devised based on the newly discovered property. Last but not least, we apply the TrajPattern algorithm on a wide range of real and synthetic data sets to demonstrate the usefulness, efficiency, and scalability of this approach.