An algorithm for displaying a class of space-filling curves
Software—Practice & Experience
Fractals for secondary key retrieval
PODS '89 Proceedings of the eighth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
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
Linear clustering of objects with multiple attributes
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Range queries in OLAP data cubes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
SAC '98 Proceedings of the 1998 ACM symposium on Applied Computing
ACM Computing Surveys (CSUR)
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Analysis of the Clustering Properties of the Hilbert Space-Filling Curve
IEEE Transactions on Knowledge and Data Engineering
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
On the Generation of Spatiotemporal Datasets
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Efficient OLAP Operations in Spatial Data Warehouses
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Indexing Spatio-Temporal Data Warehouses
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Adaptive cell-based index for moving objects
Data & Knowledge Engineering
Building real-world trajectory warehouses
Proceedings of the Seventh ACM International Workshop on Data Engineering for Wireless and Mobile Access
Visual Mobility Analysis using T-Warehouse
International Journal of Data Warehousing and Mining
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Most of the framework for supporting OLAP operations over immense amounts of spatio-temporal data is based on multi-tree structures. The multi-tree frameworks, however, are hardly applicable to spatio-temporal OLAP in practice, due mainly to high management costs and low query efficiency. To overcome the limitations of such multi-tree frameworks, we propose a new approach called ST-Cube (spatio-temporal cube), which is an adaptive cell-based, total-ordered and prefix-summed cube for spatio-temporal data warehouses. Our extensive performance studies show that the ST-Cube requires less space and achieves higher query performance than multi-tree frameworks, under various operational conditions.