Data Mining with the SAP NetWeaver BI Accelerator

VLDB 2006 - Proceedings of the 32nd International Conference on Very Large Data Bases

Volumn , Issue , 2006, Pages 1059-1068

  Legler, Thomas ^a   Lehner, Wolfgang ^a   Ross, Andrew ^b  

^a DRESDEN UNIVERSITY OF TECHNOLOGY   (Germany)

^b SAP RESEARCH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ACCELERATION; SEA ICE;

ARCHITECTURAL FEATURES; BUSINESS-INTELLIGENCE; DISTRIBUTED ARCHITECTURE; INDEX STRUCTURE; LARGE VOLUMES; NETWEAVER; PERFORMANCES EVALUATION; POWER; RUNTIMES; STRUCTURED DATA;

DATA MINING;

EID: 85134428779     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. R. Agrawal, T. Imielinski, and A. N. Swami. Mining association rules between sets of items in large databases. In P. Buneman and S. Jajodia, editors, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pages 207–216, Washington, D.C., 26–28 1993.
2. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In J. B. Bocca, M. Jarke, and C. Zaniolo, editors, Proceedings of the 20th International Conference of Very Large Data Bases, VLDB, pages 487–499. Morgan Kaufmann, 12–15 1994.
3. K. Beyer and R. Ramakrishnan. Bottom-up computation of sparse and Iceberg CUBE. In Proceedings of the ACM SIGMOD International Conference on Management of Data 1999, pages 359–370, 1999.
4. P. Boncz. MonetDB: A high performance database kernel for query-intensive applications, http://www.monetdb.com/.
5. D. W. Cheung, V. T. Ng, A. W. Fu, and Y. J. Fu. Efficient mining of association rules in distributed databases. IEEE Trans. on Knowledge and Data Engineering, 8:911–922, 1996.
6. J. Gray, S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M. Venkatrao, F. Pellow, and H. Pirahesh. Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub-totals. J. Data Mining and Knowledge Discovery, 1(1):29–53, 1997.
7. J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In W. Chen, J. Naughton, and P. A. Bernstein, editors, 2000 ACM SIGMOD International Conference on Management of Data, pages 1–12. ACM Press, 05 2000.
8. C. Hidber. Online association rule mining. In SIGMOD 1999, Proceedings ACM SIGMOD International Conference on Management of Data, Philadephia, PA, USA, pages 145–156. ACM Press, 1999.
9. W. A. Kosters and W. Pijls. Apriori, a depth first implementation. In Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations, 2003.
10. A. Mueller. Fast sequential and parallel algorithms for association rule mining: a comparison. Technical report, College Park, MD, USA, 1995.
11. W. Pijls and J. C. Bioch. Mining frequent itemsets in memory-resident databases. In Proceedings of the Eleventh Belgium /Netherlands Artificial Intelligence Conference (BNAIC 99), pages 75–82, 1999.
12. A. Savasere, E. Omiecinski, and S. B. Navathe. An efficient algorithm for mining association rules in large databases. In VLDB Journal, pages 432–444, 1995.
13. M. Stonebraker, D. J. Abadi, A. Batkin, X. Chen, M. Cherniack, M. Ferreira, E. Lau, A. Lin, S. Madden, E. O’Neil, P. O’Neil, A. Rasin, N. Tran, and S. Zdonik. C-store: a column-oriented DBMS. In VLDB’05: Proceedings of the 31st International Conference on Very Large Data Bases, pages 553–564. VLDB Endowment, 2005.
14. J. T.-L. Wang, G.-W. Chirn, T. G. Marr, B. Shapiro, D. Shasha, and K. Zhang. Combinatorial pattern discovery for scientific data: some preliminary results. In SIGMOD’94: Proceedings of the 1994 ACM SIGMOD International Conference on Management of Data, pages 115–125, New York, NY, USA, 1994. ACM Press.
15. I. H. Witten, A. Moffat, and T. C. Bell. Managing Gigabytes. Compressing and Indexing Documents and Images. Academic Press, 1999.
16. M. J. Zaki. Parallel and distributed association mining: A survey. IEEE Concurrency, 7(4):14–25, 1999.