TY - GEN
T1 - PHC
T2 - 7th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2007
AU - Hu, Kongfa
AU - Chen, Ling
AU - Chen, Yixin
PY - 2007
Y1 - 2007
N2 - Data cube has been playing an essential role in OLAP (online analytical processing). The pre-computation of data cubes is critical for improving the response time of OLAP systems. However, as the size of data cube grows, the time it takes to perform this pre-computation becomes a significant performance bottleneck. In a high dimensional OLAP, it might not be practical to build all these cuboids and their indices. In this paper, we propose a parallel hierarchical cubing algorithm, based on an extension of the previous minimal cubing approach. The algorithm has two components: decomposition of the cube space based on multiple dimension attributes, and an efficient OLAP query engine based on a prefix bitmap encoding of the indices. This method partitions the high dimensional data cube into low dimensional cube segments. Such an approach permits a significant reduction of CPU and I/O overhead for many queries by restricting the number of cube segments to be processed for both the fact table and bitmap indices. The proposed data allocation and processing model support parallel I/O and parallel processing, as well as load balancing for disks and processors. Experimental results show that the proposed parallel hierarchical cubing method is significantly more efficient than other existing cubing methods.
AB - Data cube has been playing an essential role in OLAP (online analytical processing). The pre-computation of data cubes is critical for improving the response time of OLAP systems. However, as the size of data cube grows, the time it takes to perform this pre-computation becomes a significant performance bottleneck. In a high dimensional OLAP, it might not be practical to build all these cuboids and their indices. In this paper, we propose a parallel hierarchical cubing algorithm, based on an extension of the previous minimal cubing approach. The algorithm has two components: decomposition of the cube space based on multiple dimension attributes, and an efficient OLAP query engine based on a prefix bitmap encoding of the indices. This method partitions the high dimensional data cube into low dimensional cube segments. Such an approach permits a significant reduction of CPU and I/O overhead for many queries by restricting the number of cube segments to be processed for both the fact table and bitmap indices. The proposed data allocation and processing model support parallel I/O and parallel processing, as well as load balancing for disks and processors. Experimental results show that the proposed parallel hierarchical cubing method is significantly more efficient than other existing cubing methods.
KW - Data cube
KW - High dimensional OLAP
KW - Parallel hierarchical cubing algorithm (PHC)
UR - https://www.scopus.com/pages/publications/37249075475
U2 - 10.1007/978-3-540-72905-1_7
DO - 10.1007/978-3-540-72905-1_7
M3 - Conference contribution
AN - SCOPUS:37249075475
SN - 9783540729044
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 72
EP - 82
BT - Algorithms and Architectures for Parallel Processing - 7th International Conference, ICA3PP 2007, Proceedings
PB - Springer Verlag
Y2 - 11 June 2007 through 14 June 2007
ER -