The snapshot index: an I/O-optimal access method for timeslice queries
Information Systems
An extensible notation for spatiotemporal index queries
ACM SIGMOD Record
SAC '98 Proceedings of the 1998 ACM symposium on Applied Computing
Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Proceedings of the 10th ACM international symposium on Advances in geographic information systems
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Specifications for Efficient Indexing in Spatiotemporal Databases
SSDBM '98 Proceedings of the 10th International Conference on Scientific and Statistical Database Management
STAR-Tree: An Efficient Self-Adjusting Index for Moving Objects
ALENEX '02 Revised Papers from the 4th International Workshop on Algorithm Engineering and Experiments
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Geographical (spatial) information about the real world changes rapidly with time. We can simply see examples of these changes when we look at any area. New buildings, new roads and highways, and many other new constructions are added or updated. Spatial changes can be categorized in two categories: (1) Discrete: changes of the geometries of physical entities (i.e., buildings) and (2) abstract: moving objects like airplanes, cars or even moving people. Spatio-temporal databases need to store information about spatial information and record their changes over time. The main goal our study in this paper is to find an efficient way to deal with spatio-temporal data, including the ability to store, retrieve, update, and query. We offer an approach for indexing and retrieving spatio-temporal data (AIRSTD). We concentrate on two main objectives: (1) Provide indexing structures for spatio-temporal data and (2) provide efficient algorithms to deal with these structures.