TY - GEN
T1 - A cloud-based efficient on-line analytical processing system with inverted data model
AU - Huang, Sheng Wei
AU - Shieh, Ce Kuen
AU - Liao, Che Ching
AU - Chiu, Chui Ming
AU - Tsai, Ming Fong
AU - Chen, Lien Wu
N1 - Publisher Copyright:
© 2015 ICST.
PY - 2015/11/19
Y1 - 2015/11/19
N2 - On-line analytical processing (OLAP) provides analysis of multi-dimensional data stored in a database and achieves great success in many applications such as sales, marketing, financial data analysis. OLAP operation is a dominant part of data analysis especially when addressing a large amount of data. With the emergence of the MapReduce paradigm and cloud technology, OLAP operation can be processed on big data that resides in scalable, distributed storage. However, current MapReduce implementations of OLAP operation processing have a major performance drawback caused by improper processing procedure. This is crucial when dimension or dependent attributes are large, which is a common case for most data warehouses hold nowadays. To solve this issue, this paper proposes a methodology to accelerate the performance of OLAP operation processing on big data. We have conducted the experiments on the basic algebra of OLAP operation with different data sizes to demonstrate the effectiveness of our system.
AB - On-line analytical processing (OLAP) provides analysis of multi-dimensional data stored in a database and achieves great success in many applications such as sales, marketing, financial data analysis. OLAP operation is a dominant part of data analysis especially when addressing a large amount of data. With the emergence of the MapReduce paradigm and cloud technology, OLAP operation can be processed on big data that resides in scalable, distributed storage. However, current MapReduce implementations of OLAP operation processing have a major performance drawback caused by improper processing procedure. This is crucial when dimension or dependent attributes are large, which is a common case for most data warehouses hold nowadays. To solve this issue, this paper proposes a methodology to accelerate the performance of OLAP operation processing on big data. We have conducted the experiments on the basic algebra of OLAP operation with different data sizes to demonstrate the effectiveness of our system.
UR - http://www.scopus.com/inward/record.url?scp=84962367354&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962367354&partnerID=8YFLogxK
U2 - 10.4108/eai.19-8-2015.2261409
DO - 10.4108/eai.19-8-2015.2261409
M3 - Conference contribution
AN - SCOPUS:84962367354
T3 - Proceedings of the 11th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QSHINE 2015
SP - 341
EP - 345
BT - Proceedings of the 11th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QSHINE 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QSHINE 2015
Y2 - 19 August 2015 through 20 August 2015
ER -