Algorithms and Architectures for Parallel Processing / Lecture Notes in Computer Science Bd.4494 (PDF)
This book constitutes the refereed proceedings of the 7th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2007, held in Hangzhou, China in June 2007. Focusing on two broad areas of parallel and distributed...
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This book constitutes the refereed proceedings of the 7th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2007, held in Hangzhou, China in June 2007. Focusing on two broad areas of parallel and distributed computing, the papers are organized in topical sections on parallel algorithms, parallel architecture, grid computing, peer-to-peer technologies, and advanced network technologies.
Kongfa Hu1, Ling Chen1, and Yixin Chen2
1 Department of Computer Science and Engineering, Yangzhou University, 225009, China
2 Department of Computer Science and Engineering, Washington University, 63130, USA
Abstract. 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. Keywords: data cube, parallel hierarchical cubing algorithm (PHC), high dimensional OLAP.
1 Introduction
Data warehouses integrate massive amounts of data from multiple sources and are primarily used for decision support purposes. They have to process complex analytical queries for different
- Autoren: Hai Jin , Yi Pan
- 2007, 2007, 514 Seiten, Englisch
- Verlag: Springer-Verlag GmbH
- ISBN-10: 3540729054
- ISBN-13: 9783540729051
- Erscheinungsdatum: 21.08.2007
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