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1999
IEEE

Parallel Algorithms for Computing Temporal Aggregates

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Parallel Algorithms for Computing Temporal Aggregates
The ability to model the temporal dimension is essential to many applications. Furthermore, the rate of increase in database size and response time requirements has outpaced advancements in processor and mass storage technology, leading to the need for parallel temporal database management systems. In this paper, we introduce a variety of parallel temporal aggregation algorithms for a sharednothing architecture based on the sequential Aggregation Tree algorithm. Via an empirical study, we found that the number of processing nodes, the partitioning of the data, the placement of results, and the degree of data reduction effected by the aggregation impacted the performance of the algorithms. For distributed results placement, we discovered that Time Division Merge was the obvious choice. For centralized results and high data reduction, Pairwise Merge was preferred regardless of the number of processing nodes, but for low data reduction, it only performed well up to 32 nodes. This led us ...
Jose Alvin G. Gendrano, Bruce C. Huang, Jim M. Rod
Added 01 Nov 2009
Updated 01 Nov 2009
Type Conference
Year 1999
Where ICDE
Authors Jose Alvin G. Gendrano, Bruce C. Huang, Jim M. Rodrigue, Bongki Moon, Richard T. Snodgrass
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