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Indexing and retrieval of historical aggregate information about moving objects

Authors Papadias, Dimitris
Tao, Yufei
Zhang, Jun
Mamoulis, Nikos
Shen, Qiongmao
Sun, Jimeng
Issue Date 2002
Source IEEE data on engineering bulletin (special issue on indexing of moving objects) , v. 25, (2), 2002, p. 10-17
Summary 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.
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Language English
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