Finding patterns in time series: a dynamic programming approach
Advances in knowledge discovery and data mining
Novel Approaches in Query Processing for Moving Object Trajectories
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Shape-Based Similarity Query for Trajectory of Mobile Objects
MDM '03 Proceedings of the 4th International Conference on Mobile Data Management
Optimal aggregation algorithms for middleware
Journal of Computer and System Sciences - Special issu on PODS 2001
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
SIAM Journal on Discrete Mathematics
Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Efficient mining of group patterns from user movement data
Data & Knowledge Engineering
Computing longest duration flocks in trajectory data
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Discovery of Periodic Patterns in Spatiotemporal Sequences
IEEE Transactions on Knowledge and Data Engineering
Similarity Search in Trajectory Databases
TIME '07 Proceedings of the 14th International Symposium on Temporal Representation and Reasoning
Efficient k-nearest-neighbor search algorthims for historical moving object trajectories
Journal of Computer Science and Technology
Discovery of convoys in trajectory databases
Proceedings of the VLDB Endowment
Trajectory Outlier Detection: A Partition-and-Detect Framework
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Convoy Queries in Spatio-Temporal Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Efficient processing of probabilistic reverse nearest neighbor queries over uncertain data
The VLDB Journal — The International Journal on Very Large Data Bases
WhereNext: a location predictor on trajectory pattern mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
On-line discovery of flock patterns in spatio-temporal data
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Efficient algorithms for historical continuous kNN query processing over moving object trajectories
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Mining periodic behaviors for moving objects
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
T-drive: driving directions based on taxi trajectories
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Efficient k-nearest neighbor search on moving object trajectories
The VLDB Journal — The International Journal on Very Large Data Bases
Swarm: mining relaxed temporal moving object clusters
Proceedings of the VLDB Endowment
Driving with knowledge from the physical world
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Ranking continuous nearest neighbors for uncertain trajectories
The VLDB Journal — The International Journal on Very Large Data Bases
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Efficiently processing snapshot and continuous reverse k nearest neighbors queries
The VLDB Journal — The International Journal on Very Large Data Bases
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Nowadays, massive amounts of tracking data for various types of moving objects, including vehicles, humans and animals, are becoming available. Analyzing this type of spatio-temporal data is crucial for discovering movement patterns, understanding and forecasting behaviors, and developing novel applications and services. One problem of particular interest is finding objects that move close together with a certain object during some periods of time. In this paper, we focus on finding the k-nearest moving neighbors for a given query object and time interval. We formulate the problem, using a similarity function that takes into consideration both the proximity and the direction of the trajectories, and we firstly present an exact algorithm. Then, we focus on approximate algorithms in order to reduce the execution time, investigating two directions. The first employs line simplification to approximate the compared trajectories, thus reducing the calculations needed to identify the nearest neighbors. The second relies on estimates of prior probabilities derived from trajectory distributions and attempts to achieve a faster approximation of the k-nearest neighbors. A detailed experimental evaluation of the aforementioned algorithms on three real-world datasets is finally presented in order to verify their efficiency and accuracy.