p-MDAG - A Parallel MDAG Approach

Joubert de Castro Lima, Celso Massaki Hirata

2010

Abstract

In this paper, we present a novel parallel full cube computation approach, named p-MDAG. The p-MDAG approach is a parallel version of MDAG sequential approach. The sequential MDAG approach outperforms the classic Star approach in dense, skewed and sparse scenarios. In general, the sequential MDAG approach is 25-35% faster than Star, consuming, on average, 50% less memory to represent the same data cube. The p-MDAG approach improves the runtime while keeping the low memory consumption; it uses an attribute-based data cube decomposition strategy which combines both task and data parallelism. The p-MDAG approach uses the dimensions attribute values to partition the data cube. It also redesigns the MDAG sequential algorithms to run in parallel. The p-MDAG approach provides both good load balance and similar sequential memory consumption. Its logical design can be implemented in shared-memory, distributed-memory and hybrid architectures with minimal adaptation.

References

  1. Beyer, K. and Ramakrishnan, R. Bottom-up computation of sparse and Iceberg CUBEs. SIGMOD, 28(2):359- 371, 1999.
  2. Chen, Y., Dehne, F., Eavis, T. and Rau-Chaplin, A. Parallel ROLAP data cube construction on sharednothing multiprocessors. Distributed and Parallel Databases, 15:219-236, 2004.
  3. Chen, Y., Dehne, F., Eavis, T. and Rau-Chaplin, A. PnP: sequential, external memory, and parallel iceberg cube computation. Distributed and Parallel Databases, 23(2):99-126, 2008.
  4. Dehne, F., Eavis, T., and Rau-Chaplin, A. Parallelizing the data cube. Distributed and Parallel Databases, 11(2):181-201, 2002.
  5. Dehne, F., Eavis, T., Hambrush, S. and Rau-Chaplin, A. Parallelizing the data cube. International Conference on Database Theory, 2001.
  6. DeWitt, D. and Gray, J. Parallel database systems: the future of high performance database systems. Communications of the ACM, 35(6):85-98, 1992.
  7. Dongarra, J., Foster, I., Fox, G., Gropp, W., Kennedy, K., Torczon, L. and White, A. Source Book of Parallel Computing. Morgan Kaufman, 2003.
  8. Goil, S. and Choudhary, A. High performance OLAP and data mining on parallel computers. Journal of Data Mining and Knowledge Discovery, (4), 1997.
  9. Goil, S. and Choudhary, A. High performance multidimensional analysis of large datasets. First ACM International Workshop on Data Warehousing and OLAP, pages 34-39, 1998.
  10. Goil, S. and Choudhary, A. A parallel scalable infrastructure for OLAP and data mining. International Database Engineering and Application Symposium, pages 178-186, 1999.
  11. Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., Venkatrao, M., Pellow, F. and Pirahesh, H. Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub totals. Data Mining and Knowledge Discover, 1(1):29-53, 1997.
  12. Han, J., Pei, J., Dong, G. and Wang, K. Efficient computation of iceberg cubes with complex measures. SIGMOD, pages 1-12. ACM, 2001.
  13. Han, J., Kamber, M. Data Mining Concepts and Techniques. Morgan Kaufman, 2006.
  14. Lakshmanan, L.V.S., Pei, J. and Han, J. Quotient cube: How to summarize the semantics of a data cube. VLDB'02, pages 778-789. Morgan Kaufmann, 2002.
  15. Lima, J.C. and Hirata, C.M. MDAG-Cubing: A Reduced Star-Cubing Approach. SBBD, 362-376, October 2007.
  16. Lu, H., Yu, J., Feng, L. and Li, X. Fully dynamic partitioning: Handling data skew in parallel data cube computation. Distributed and Parallel Databases, 13:181-202, 2003.
  17. Li, X., Han, J. and Gonzalez, H. High-dimensional OLAP: A minimal cubing approach. In VLDB'04, pages 528- 539. Morgan Kaufmann, 2004.
  18. Muto, S. and Kitsuregawa, M. A dynamic load balancing strategy for parallel data cube computation. ACM 2nd Annual Workshop on Data Warehousing and OLAP, pages 67-72, 1999.
  19. Olken, F., and Rotem, D. Random sampling from database files - a survey. In 5th International Conference on Statistical and Scientific Database Management, 1990.
  20. Xin, D., Han, J., Li, X. and Wah, B.W. Star-Cubing: Computing Iceberg Cubes by Top-Down and BottomUp Integration. VLDB'03, pages 476-487. Morgan Kaufmann, 2003.
  21. Xin, D., Han, J., Li, X., Shao, Z. and Wah, B.W. Computing Iceberg Cubes by Top-Down and BottomUp Integration: The StarCubing Approach. IEEE Transactions on Knowledge and Data Engineering, 19(1): 111-126, 2007.
  22. Xin, D., Shao, Z., Han, J., and Liu, H. C-cubing: Efficient computation of closed cubes by aggregation-based checking. ICDE'06, page 4. IEEE Computer Society, 2006.
  23. Zhao, Y., Deshpande, P., and Naughton, J. F. An arraybased algorithm for simultaneous multidimensional aggregates. SIGMOD, pages 159-170. ACM, 1997.
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Paper Citation


in Harvard Style

de Castro Lima J. and Hirata C. (2010). p-MDAG - A Parallel MDAG Approach . In Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-8425-04-1, pages 322-331. DOI: 10.5220/0003017703220331


in Bibtex Style

@conference{iceis10,
author={Joubert de Castro Lima and Celso Massaki Hirata},
title={p-MDAG - A Parallel MDAG Approach},
booktitle={Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2010},
pages={322-331},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003017703220331},
isbn={978-989-8425-04-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - p-MDAG - A Parallel MDAG Approach
SN - 978-989-8425-04-1
AU - de Castro Lima J.
AU - Hirata C.
PY - 2010
SP - 322
EP - 331
DO - 10.5220/0003017703220331