The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Near Neighbor Search in Large Metric Spaces
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
SEA-CNN: Scalable Processing of Continuous K-Nearest Neighbor Queries in Spatio-temporal Databases
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
A generic framework for monitoring continuous spatial queries over moving objects
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
A trajectory splitting model for efficient spatio-temporal indexing
VLDB '05 Proceedings of the 31st international conference on Very large data bases
ST2B-tree: a self-tunable spatio-temporal b+-tree index for moving objects
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Supporting Pattern-Matching Queries over Trajectories on Road Networks
IEEE Transactions on Knowledge and Data Engineering
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With the advent of ubiquitous computing, we can easily collect large scale trajectory data from moving vehicles. This paper presents TPM (Trajectory Pattern Miner), a software aimed at pattern matching queries for road-network trajectory data, which complements existing efforts focusing on (a) a spatio-temporal window query for location-based service or (b) Euclidean space with no restriction. To overcome limitations of prior research, TPM supports three types of pattern matching queries-- whole, subpattern, and reverse sub-pattern matching for road-network trajectories. We demonstrate application scenarios for each type of pattern matching queries using large-scale real-life trajectory data.