A data model and data structures for moving objects databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Spatio-Temporal Data Handling with Constraints
Geoinformatica
IEEE Transactions on Knowledge and Data Engineering
Specifications for Efficient Indexing in Spatiotemporal Databases
SSDBM '98 Proceedings of the 10th International Conference on Scientific and Statistical Database Management
Moving Objects: Logical Relationships and Queries
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
The Retrieval of Direction Relations using R-trees
DEXA '94 Proceedings of the 5th International Conference on Database and Expert Systems Applications
Modeling of Moving Objects in a Video Database
ICMCS '97 Proceedings of the 1997 International Conference on Multimedia Computing and Systems
Composing cardinal direction relations
Artificial Intelligence
A Family of Directional Relation Models for Extended Objects
IEEE Transactions on Knowledge and Data Engineering
Evaluation of cardinal direction developments between moving points
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
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In the same way as moving objects can change their location over time, the spatial relationships between them can change over time. An important class of spatial relationships are cardinal directions like north and southeast. In spatial databases and GIS, they characterize the relative directional position between static objects in space and are frequently used as selection and join criteria in spatial queries. Transferred to a spatiotemporal context, the simultaneous location change of different moving objects can imply a temporal evolution of their directional relationships, called development. In this paper, we provide an algorithmic solution for determining such a temporal development of cardinal directions between two moving points. Based on the slice representation of moving points, our solution consists of three phases, the time-synchronized interval refinement phase for synchronizing the time intervals of two moving points, the slice unit direction evaluation phase for computing the cardinal directions between two slice units that are defined in the same time interval from both moving points, and finally the direction composition phase for composing the cardinal directions computed from each slice unit pair. Finally, we show the integration of spatio-temporal cardinal directions into spatio-temporal queries as spatio-temporal directional predicates, and present a case study on the hurricane data.