Proceedings 18th International Conference on Data Engineering (2002)
San Jose, California
Feb. 26, 2002 to Mar. 1, 2002
Dimitris Papadias , Hong Kong University of Science and Technology
Yufei Tao , Hong Kong University of Science and Technology
Panos Kalnis , Hong Kong University of Science and Technology
Jun Zhang , Hong Kong University of Science and Technology
Spatio-temporal databases store information about the positions of individual objects over time. In many applications however, such as traffic supervision or mobile communication systems, only summarized data, like the average number of cars in an area for a specific period, or phones serviced by a cell each day, is required. Although this information can be obtained from operational databases, its computation is expensive, rendering online processing inapplicable. A vital solution is the construction of a spatiotemporal data warehouse. In this paper, we describe a framework for supporting OLAP operations over spatiotemporal data. We argue that the spatial and temporal dimensions should be modeled as a combined dimension on the data cube and present data structures, which integrate spatiotemporal indexing with pre-aggregation. While the well-known materialization techniques require a-priori knowledge of the grouping hierarchy, we develop methods that utilize the proposed structures for efficient execution of ad-hoc group-bys. Our techniques can be used for both static and dynamic dimensions.
Spatio-temporal datawaehouses, OLAP, data structures
Y. Tao, J. Zhang, P. Kalnis and D. Papadias, "Indexing Spatio-Temporal Data Warehouses," Proceedings 18th International Conference on Data Engineering(ICDE), San Jose, California, 2002, pp. 0166.