Parallel processing of multiple aggregate queries on shared-nothing multiprocessors

  • Fukuda Takeshi
  • Hirofumi Matsuzawa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1377)

Abstract

Decision support systems that include on-line analytical processing and data mining have recently attracted research attention. Such applications treat data in very large databases as multidimensional data cubes. Each cell of a data cube typically is some aggregation, such as total sales volume, that is of interest to analysts. Since it may be necessary to compute many cells, and the performance is critical, we propose parallel algorithms that compute multiple aggregate queries in data cubes on a shared-nothing multiprocessor with high-bandwidth communication facilities. We evaluate the algorithms on the basis of analytical modeling and an implementation on an IBM SP2 system.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Tilak Agerwala, Joanne L. Martin, Jamshed H. Mirza, David C. Sadler, Daniel M. Dias, and Marc Snir. SP2 system architecture. IBM Systems Journal, 34(2):152–184, 95.Google Scholar
  2. 2.
    Sameet Agrawal, Rakesh Agrawal, Prasad M. Deshpande, Ashish Gupta, Jeffrey F. Naughton, Raghu Ramakrishnan, and Sunita Sarawagi. On the computation of multidimensional aggregates. In Proceedings of the 22nd VLDB Conference, September 1996.Google Scholar
  3. 3.
    Dina Bitton, Haran Boral, David J. DeWitt, and W. Kevin Wilkinson. Parallel algorithms for the excecution of relational database operations. ACM Trans. on Database Systems, 8(3):324–353, September 1983.CrossRefGoogle Scholar
  4. 4.
    E. F. Codd, S. B. Codd, and C. T. Salley. Beyond decision support. Computer-world, 27(30), July 1993.Google Scholar
  5. 5.
    Takeshi Fukuda, Yasuhiko Morimoto, Shinichi Morishita, and Takeshi Tokuyama. Constructing efficient decision trees by using optimized association rules. In Proceedings of the 22nd VLDB Conference, pages 146–155, 1996.Google Scholar
  6. 6.
    Takeshi Fukuda, Yasuhiko Morimoto, Shinichi Morishita, and Takeshi Tokuyama. Data mining using two-dimensional optimized association rules: Scheme, algorithms, and visualization. In Proceedings of the ACM SIGMOD Conference on Management of Data, pages 13–23, June 1996.Google Scholar
  7. 7.
    Takeshi Fukuda, Yasuhiko Morimoto, Shinichi Morishita, and Takeshi Tokuyama. Mining optimized association rules for numeric attributes. In Proceedings of the Fifteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pages 182–191, June 1996.Google Scholar
  8. 8.
    Takeshi Fukuda, Yasuhiko Morimoto, Shinichi Morishita, and Takeshi Tokuyama. Sonar: System for optimized numeric association rules. In Proceedings of the ACM SIGMOD Conference on Management of Data, page 553, June 1996.Google Scholar
  9. 9.
    Goetz Graefe. Query evaluation techniques for large databases. ACM Computing Surveys, 25(2):73–170, June 1993.CrossRefGoogle Scholar
  10. 10.
    Jim Gray, Adam Bosworth, Andrew Layman, and Hamid Pirahesh. Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub-totals. Technical report, Microsoft, November 1995.Google Scholar
  11. 11.
    Ashish Gupta, Venky Harinarayan, and Dallan Quass. Aggregate-query processing in data warehousing environments. In Proceedings of the 21st VLDB Conference, pages 358–369, 1995.Google Scholar
  12. 12.
    Himanshu Gupta, Venky Harinarayan, Anand Rajaraman, and Jeffrey D. Ullman. Index selection for OLAP. Working Paper, 1996.Google Scholar
  13. 13.
    Peter J. Haas, Jeffrey F. Naughton, S. Seshadri, and Lynne Stokes. Sampling-based estimation of the number of distinct values of an attribute. In Proceedings of the 21st VLDB Conference, pages 311–322, 1995.Google Scholar
  14. 14.
    Venky Harinarayan, Anand Rajaraman, and Jeffrey D. Ullman. Implementing data cubes efficiently. In Proceedings of the ACM SIGMOD Conference on Management of Data, pages 205–216, June 1996.Google Scholar
  15. 15.
    Theodore Johnson and Dennis Shasha. Hierarchically split cube forests for decision support: description and tuned design. Working Paper, 1996.Google Scholar
  16. 16.
    Message Passing Interface Forum. MPI: A Message-Passing Interface Standard, May 1994.Google Scholar
  17. 17.
    Yasuhiko Morimoto, Hiromu Ishii, and Shinichi Morishita. Efficient construction of regression trees with range and region splitting. In Proceedings of the 23rd VLDB Conference, pages 166–175, August 1997.Google Scholar
  18. 18.
    Sunita Sarawagi, Rakesh Agrawal, and Ashish Gupta. On computing the data cube. Technical Report RJ10026, IBM Almaden Research Center, 1996.Google Scholar
  19. 19.
    Ambuj Shatdal and Jeffrey F. Naughton. Adaptive parallel aggregation algorithms. In Proceedings of the ACM SIGMOD Conference on Management of Data, pages 104–114, May 1995.Google Scholar
  20. 20.
    Cralg B. Stunkel, Dennis G. Shea, Bülent Abali, Mark G. Atkins, Carl A. Bender, Don G. Grice, Peter Hochschild, Doug J. Joseph, Ben J. Nathanson, Richard A. Swetz, Robert F. Stucke, Mickey Tsao, and Philip R. Varker. The SP2 high-performance switch. IBM Systems Journal, 34(2):185–204, 95.Google Scholar
  21. 21.
    Kunikazu Yoda, Takeshi Fukuda, Yasuhiko Morimoto, Shinichi Morishita, and Takeshi Tokuyama. Computing optimized rectilinear regions for association rules. In Proceedings, Third International Conference on Knowledge Discovery and Data Mining, pages 96–103, August 1997.Google Scholar

Copyright information

© Springer-Verlag 1998

Authors and Affiliations

  • Fukuda Takeshi
    • 1
  • Hirofumi Matsuzawa
    • 1
  1. 1.IBM Tokyo Research LaboratoryKanagawa Pref.Japan

Personalised recommendations