Requirements, definitions, and notations for spatiotemporal application environments
Proceedings of the 6th ACM international symposium on Advances in geographic information systems
Advanced database indexing
Overlapping linear quadtrees and spatio-temporal query processing
The Computer Journal
The K-D-B-tree: a search structure for large multidimensional dynamic indexes
SIGMOD '81 Proceedings of the 1981 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Indexing Animated Objects Using Spatiotemporal Access Methods
IEEE Transactions on Knowledge and Data Engineering
MV3R-Tree: A Spatio-Temporal Access Method for Timestamp and Interval Queries
Proceedings of the 27th International Conference on Very Large Data Bases
Specifications for Efficient Indexing in Spatiotemporal Databases
SSDBM '98 Proceedings of the 10th International Conference on Scientific and Statistical Database Management
On the Generation of Spatiotemporal Datasets
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Spatio-Temporal Indexing for Large Multimedia Applications
ICMCS '96 Proceedings of the 1996 International Conference on Multimedia Computing and Systems
SSDBM '01 Proceedings of the 13th International Conference on Scientific and Statistical Database Management
Indexing problems in spatiotemporal databases
Indexing problems in spatiotemporal databases
A meta-index for querying distributed moving object database servers
Information Systems
The SMO-index: a succinct moving object structure for timestamp and interval queries
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
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This paper describes a new spatio-temporal access method (SEST-Index) that combines two approaches for modeling spatio-temporal information: snapshots and events. This method makes it possible to not only process time slice and interval queries, but also queries about events. The SEST Index implementation uses an R-tree structure for storing snapshots and a log data structure for storing events that occur between consecutive snapshots. Experimental results that compare SEST-Index and HR-tree show that, for a change frequency between 1% and 13%, SEST-Index requires less storage space than HR-tree, and for a change frequency between 1% and 7%, SEST-Index outperforms HR-tree for interval queries. In addition, as SEST-Index is an event-oriented structure, event queries are efficiently answered. In order to decrease the storage space for frequencies of change above 20%, this work explores alternatives that optimize the space of the log structure without affecting the efficiency of query answers.