Parallel algorithms for computing temporal aggregates

Gendrano, Jose Alvin G.; Huang, Bruce C.; Rodrigue, Jim M.; Moon, Bongki; Snodgrass, Richard T.

doi:10.1109/icde.1999.754958

Cited by 17 publications

(8 citation statements)

References 6 publications

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“…The above techniques target point/interval objects (they are inapplicable to regions), while Zhang et al [Kline and Snodgrass 1995, Gendrano et al 1999, Moon et al 2000, Yang and Widom 2003. Zhang et al , Zhang et al 2003 study spatial and temporal aggregation over data streams.…”

Section: Multi-dimensional Aggregate Methodsmentioning

confidence: 99%

Historical spatio-temporal aggregation

Tao

Papadias

2005

ACM Trans. Inf. Syst.

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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 preaggregation. 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.

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Section: Multi-dimensional Aggregate Methodsmentioning

confidence: 99%

Historical spatio-temporal aggregation

Tao

Papadias

2005

ACM Trans. Inf. Syst.

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“…Previous work on temporal aggregation [9,14,17] mainly focussed on queries that aggregate over the whole range in all non-temporal dimensions.…”

Section: Related Workmentioning

confidence: 99%

Efficient integration and aggregation of historical information

Riedewald

Agrawal

Abbadi

2002

Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data

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A B S T R A C TData warehouses support the analysis of historical data. This often involves aggregation over a period of time. Furthermore, data is typically incorporated in the warehouse in the increasing order of a time attribute, e.g., date of a sale or time of a temperature measurement. In this paper we propose a framework to take advantage of this appendonly nature of updates due to a time attribute. The framework allows us to integrate large amounts of new data into the warehouse and generate historical summaries efficiently. Query and update costs are virtually independent from the extent of the data set in the time dimension, making our framework an attractive aggregation approach for appendonly data streams. A specific instantiation of the general approach is developed for MOLAP data cubes, involving a new data structure for append-only arrays with pre-aggregated values. Our framework is applicable to point data and data with extent, e.g., hyper-rectangles.

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“…For example, assume that the value of time is one digit. Then, the T-value for the time interval of a record, [3,4], is 34. If the value of time is a two-digit number, the T-value will be 0304.…”

Section: T-value Generationmentioning

confidence: 99%

“…Recent research on the TDB has considered temporal operations including temporal aggregation and their processing techniques [4,10,11,13,17,20,[24][25][26].…”

Section: Introductionmentioning

confidence: 99%