Main memory-based algorithms for efficient parallel aggregation for temporal databases

The ability to model the temporal dimension is essential to many applications. Furthermore, the rate of increase in database size and stringency of response time requirements has out-paced advancements in processor and mass storage technology, leading to the need for parallel temporal database manag...

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Main Authors: Gao, Dengfeng, Gendrano, Jose Alvin G., Moon, Bongki, Snodgrass, Richard T., Park, Mineosk, Huang, Bruce C., Rodrigue, Jim M.
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Published: Animo Repository 2004
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Online Access:https://animorepository.dlsu.edu.ph/faculty_research/6352
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Institution: De La Salle University
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spelling oai:animorepository.dlsu.edu.ph:faculty_research-71402022-07-15T00:36:44Z Main memory-based algorithms for efficient parallel aggregation for temporal databases Gao, Dengfeng Gendrano, Jose Alvin G. Moon, Bongki Snodgrass, Richard T. Park, Mineosk Huang, Bruce C. Rodrigue, Jim M. The ability to model the temporal dimension is essential to many applications. Furthermore, the rate of increase in database size and stringency of 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 the shared-nothing architecture; these algorithms are based on the sequential Aggregation Tree algorithm. We are particularly interested in developing parallel algorithms that can maximally exploit available memory to quickly compute large-scale temporal aggregates without intermediate disk writes and reads. 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 result placement, we discovered that Greedy Time Division Merge was the obvious choice. For centralized results and high data reduction, Pairwise Merge was preferred for a large number of processing nodes; for low data reduction, it only performed well up to 32 nodes. This led us to a centralized variant of Greedy Time Division Merge which was best for the remaining cases. We present a cost model that closely predicts the running time of Greedy Time Division Merge. 2004-09-01T07:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/6352 Faculty Research Work Animo Repository Temporal databases Parallel algorithms Computer Sciences
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Temporal databases
Parallel algorithms
Computer Sciences
spellingShingle Temporal databases
Parallel algorithms
Computer Sciences
Gao, Dengfeng
Gendrano, Jose Alvin G.
Moon, Bongki
Snodgrass, Richard T.
Park, Mineosk
Huang, Bruce C.
Rodrigue, Jim M.
Main memory-based algorithms for efficient parallel aggregation for temporal databases
description The ability to model the temporal dimension is essential to many applications. Furthermore, the rate of increase in database size and stringency of 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 the shared-nothing architecture; these algorithms are based on the sequential Aggregation Tree algorithm. We are particularly interested in developing parallel algorithms that can maximally exploit available memory to quickly compute large-scale temporal aggregates without intermediate disk writes and reads. 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 result placement, we discovered that Greedy Time Division Merge was the obvious choice. For centralized results and high data reduction, Pairwise Merge was preferred for a large number of processing nodes; for low data reduction, it only performed well up to 32 nodes. This led us to a centralized variant of Greedy Time Division Merge which was best for the remaining cases. We present a cost model that closely predicts the running time of Greedy Time Division Merge.
format text
author Gao, Dengfeng
Gendrano, Jose Alvin G.
Moon, Bongki
Snodgrass, Richard T.
Park, Mineosk
Huang, Bruce C.
Rodrigue, Jim M.
author_facet Gao, Dengfeng
Gendrano, Jose Alvin G.
Moon, Bongki
Snodgrass, Richard T.
Park, Mineosk
Huang, Bruce C.
Rodrigue, Jim M.
author_sort Gao, Dengfeng
title Main memory-based algorithms for efficient parallel aggregation for temporal databases
title_short Main memory-based algorithms for efficient parallel aggregation for temporal databases
title_full Main memory-based algorithms for efficient parallel aggregation for temporal databases
title_fullStr Main memory-based algorithms for efficient parallel aggregation for temporal databases
title_full_unstemmed Main memory-based algorithms for efficient parallel aggregation for temporal databases
title_sort main memory-based algorithms for efficient parallel aggregation for temporal databases
publisher Animo Repository
publishDate 2004
url https://animorepository.dlsu.edu.ph/faculty_research/6352
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