Indexing Spatio-Temporal Data Warehouses Dimitris Papadias, Yufei Tao, Panos Kalnis, and Jun Zhang   View Paper (PDF)   Return to Session 5: Data Warehousing and OLAP Abstract 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 the number of 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 spatio-temporal data warehouse. In this paper, we describe a framework for supporting OLAP operations over spatio-temporal data. We argue that the spatial and temporal dimensions should be modeled as a combined dimension on the data cube and we present data structures which integrate spatio-temporal 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. DiSC'03 © 2003 Association for Computing Machinery