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Parallel Algorithms for Computing Temporal Aggregates (0)
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@MISC{Alvin_parallelalgorithms,
author = {Jose Alvin and G. Gendrano and Bruce C. Huang and Jim M. Rodrigue and Bongki Moon and Richard T. Snodgrass},
title = {Parallel Algorithms for Computing Temporal Aggregates},
year = {}
}
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Abstract
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 out-paced 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 shared-nothing 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 in different ways. We designed the Time Division Merge algorithm to produce distributed result placement, as differentiated from the centralized result strategies used by the other proposed algorithms. For centralized results and high data reduction, we found that the Pairwise Merge algorithm was pre...
Keyphrases
temporal aggregate parallel algorithm parallel temporal database management system out-paced advancement mass storage technology high data reduction centralized result database size centralized result strategy sequential aggregation tree algorithm parallel temporal aggregation algorithm temporal dimension data reduction many application different way empirical study pairwise merge algorithm time division merge algorithm distributed result placement response time requirement shared-nothing architecture
