Abstract
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.
© 2005 ACM
© 2005 ACM
| Original language | English |
|---|---|
| Pages (from-to) | 61-102 |
| Journal | ACM Transactions on Information Systems |
| Volume | 23 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jan 2005 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Funding
This research was supported by the grants CityU 1163/04E and HKUST 6197/02E from Hong Kong RGC.
Research Keywords
- Access methods
- Additional Key Words and Phrases: Aggregation
- Cost models
RGC Funding Information
- RGC-funded
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