Abstract
Data cube plays a key role in the analysis of multidimensional data. Nowadays, the explosive growth of multidimensional data has made distributed solutions important for data cube computation. Among the architectures for distributed processing, the shared-nothing architecture is known to have the best scalability. However, frequent and massive network communication among the processors can be a performance bottleneck in shared-nothing distributed processing. Therefore, suppressing the amount of data transmission among the processors can be an effective strategy for improving overall performance. In addition, dividing the workload and distributing them evenly to the processors is important. In this paper, the authors present a distributed algorithm for data cube computation that can be adopted in shared-nothing systems. The proposed algorithm gains efficiency by adopting the workload assignment strategy that reduces the total network cost and allocates the workload evenly to each processor, simultaneously.
| Original language | English |
|---|---|
| Pages (from-to) | 51-71 |
| Number of pages | 21 |
| Journal | International Journal of Data Warehousing and Mining |
| Volume | 12 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2016 Jul 1 |
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Keywords
- Data Cube
- Data Warehouse
- Distributed Processing
- OLAP
- ROLAP
- Shared-Nothing Architecture
ASJC Scopus subject areas
- Software
- Hardware and Architecture
Cite this
A workload assignment strategy for efficient ROLAP data cube computation in distributed systems. / Suh, Ilhyun; Chung, Yon Dohn.
In: International Journal of Data Warehousing and Mining, Vol. 12, No. 3, 01.07.2016, p. 51-71.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A workload assignment strategy for efficient ROLAP data cube computation in distributed systems
AU - Suh, Ilhyun
AU - Chung, Yon Dohn
PY - 2016/7/1
Y1 - 2016/7/1
N2 - Data cube plays a key role in the analysis of multidimensional data. Nowadays, the explosive growth of multidimensional data has made distributed solutions important for data cube computation. Among the architectures for distributed processing, the shared-nothing architecture is known to have the best scalability. However, frequent and massive network communication among the processors can be a performance bottleneck in shared-nothing distributed processing. Therefore, suppressing the amount of data transmission among the processors can be an effective strategy for improving overall performance. In addition, dividing the workload and distributing them evenly to the processors is important. In this paper, the authors present a distributed algorithm for data cube computation that can be adopted in shared-nothing systems. The proposed algorithm gains efficiency by adopting the workload assignment strategy that reduces the total network cost and allocates the workload evenly to each processor, simultaneously.
AB - Data cube plays a key role in the analysis of multidimensional data. Nowadays, the explosive growth of multidimensional data has made distributed solutions important for data cube computation. Among the architectures for distributed processing, the shared-nothing architecture is known to have the best scalability. However, frequent and massive network communication among the processors can be a performance bottleneck in shared-nothing distributed processing. Therefore, suppressing the amount of data transmission among the processors can be an effective strategy for improving overall performance. In addition, dividing the workload and distributing them evenly to the processors is important. In this paper, the authors present a distributed algorithm for data cube computation that can be adopted in shared-nothing systems. The proposed algorithm gains efficiency by adopting the workload assignment strategy that reduces the total network cost and allocates the workload evenly to each processor, simultaneously.
KW - Data Cube
KW - Data Warehouse
KW - Distributed Processing
KW - OLAP
KW - ROLAP
KW - Shared-Nothing Architecture
UR - http://www.scopus.com/inward/record.url?scp=84991687218&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84991687218&partnerID=8YFLogxK
U2 - 10.4018/IJDWM.2016070104
DO - 10.4018/IJDWM.2016070104
M3 - Article
AN - SCOPUS:84991687218
VL - 12
SP - 51
EP - 71
JO - International Journal of Data Warehousing and Mining
JF - International Journal of Data Warehousing and Mining
SN - 1548-3924
IS - 3
ER -