Efficient processing of spatial joins using R-trees
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Spatial joins using seeded trees
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Supporting electronic ink databases
Information Systems
A foundation for representing and querying moving objects
ACM Transactions on Database Systems (TODS)
Trajectory queries and octagons in moving object databases
Proceedings of the eleventh international conference on Information and knowledge management
Indexing Animated Objects Using Spatiotemporal Access Methods
IEEE Transactions on Knowledge and Data Engineering
Efficient Indexing of Spatiotemporal Objects
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Modeling and Querying Moving Objects
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Spatial Joins Using R-trees: Breadth-First Traversal with Global Optimizations
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Novel Approaches in Query Processing for Moving Object Trajectories
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
MV3R-Tree: A Spatio-Temporal Access Method for Timestamp and Interval Queries
Proceedings of the 27th International Conference on Very Large Data Bases
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Indexing multi-dimensional time-series with support for multiple distance measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Location Based Services
Symbolic representation and retrieval of moving object trajectories
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Efficient trajectory joins using symbolic representations
Proceedings of the 6th international conference on Mobile data management
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
Dynamics-aware similarity of moving objects trajectories
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Continuous Spatiotemporal Trajectory Joins
GeoSensor Networks
Discovery of convoys in trajectory databases
Proceedings of the VLDB Endowment
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 mutual nearest neighbor query processing for moving object trajectories
Information Sciences: an International Journal
Warehousing and querying trajectory data streams with error estimation
Proceedings of the fifteenth international workshop on Data warehousing and OLAP
A framework of traveling companion discovery on trajectory data streams
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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Many spatiotemporal applications store moving object data in the form of trajectories. Various recent works have addressed interesting queries on trajectorial data, mainly focusing on range queries and Nearest Neighbor queries. Here we examine another interesting query, the Time Relaxed Spatiotemporal Trajectory Join (TRSTJ) which effectively finds groups of moving objects that have followed similar movements in different times. We first attempt to address the TRSTJ problem using a symbolic representation algorithm, which we have recently proposed for trajectory joins. However we show experimentally that this solution produces false positives that grow rapidly with the increase of the problem size. As a result, it is inefficient for TRSTJ queries as it leads to large query time overhead. In order to improve query performance, we propose two important heuristics that turn the symbolic represenation approach effective for TRSTJ queries. Our first improvement, allows the use of multiple origins when processing strings representing trajectories. The experimental evaluation shows that the multiple-origin approach drastically reduces query performance. We then present a ``divide and conquer'' approach to further reduce false positives through symbolic class separation. The proposed solutions can be combined together, which leads to even better query performance. We present an experimental study revealing the advantages of using these approaches for solving Time Relaxed Spatiotemporal Trajectory Join queries.