Spatio-temporal databases store information about the positions of individual objects over time. However, in many applications such as traffic supervision or mobile communication systems, only summarized data, like the number of cars in an area for a specific period, or phone-calls 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. In this paper, we present specialized methods, which integrate spatio-temporal indexing with pre-aggregation. The methods support dynamic spatio-temporal dimensions for the efficient processing of historical aggregate queries without a priori knowledge of grouping hierarchies. The superiority of the proposed techniques over existing methods is demonstrated through a comprehensive probabilistic analysis and an extensive experimental evaluation.
Tao and Papadias integrated spatiotemporal indexes with pre-aggregation, and proposed a method supporting dynamic spatiotemporal dimensions for the efficient processing of historical aggregation query without a priori knowledge of grouping hierarchies REF .
Historical spatio-temporal aggregation
TOIS
Computer Science