GORDIAN: Efficient and Scalable Discovery of Composite Keys

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

Volumn , Issue , 2006, Pages 691-702

  Sismanis, Yannis ^a   Brown, Paul ^a   Haas, Peter J ^a   Reinwald, Berthold ^a  

^a IBM ALMADEN RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANOMALY DETECTION; DATA INTEGRATION; INFORMATION MANAGEMENT; QUERY PROCESSING;

ANOMALY DETECTION; KEY ATTRIBUTES; LARGE DATASETS; MANAGEMENT TASKS; MODELING DATA; QUERIES OPTIMIZATION; QUERY FORMULATION; QUERY INDEXING; REAL-WORLD DATABASE; SIMPLE++;

LARGE DATASET;

EID: 85009444893     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (33)

1. S. Abiteboul, R. Hull, and V. Vianu. Foundations of Databases. Addison-Wesley, 1995.
2. A. Aboulnaga, P. J. Haas, M. Kandil, S. Lightstone, G. Lohman, V. Markl, I. Popivanov,, and V. Raman. Automated statistics collection in DB2 UDB. In Proc. VLDB, pages 1146–1157, 2004.
3. S. Bell and P. Brockhausen. Discovery of constraints and data dependencies in databases. In Proc. ECML, pages 267–270, 1995.
4. P. Brown, P. Haas, J. Myllymaki, H. Pirahesh, B. Reinwald, and Y. Sismanis. Toward automated large-scale information integration and discovery. In T. Härder and W. Lehner, editors, Data Management in a Connected World. Springer, 2005.
5. N. Bruno and S. Chaudhuri. Exploiting statistics on query expressions for optimization. In Proc. ACM SIGMOD, pages 263–274, 2002.
6. N. Bruno, S. Chaudhuri, and L. Gravano. STHoles: A multidimensional workload-aware histogram. In Proc. ACM SIGMOD, 2001.
7. S. Chaudhuri and V. Narasayya. Automating statistics management for query optimizers. IEEE Trans. Knowl. Data Engrg., 13:7–20, 2001.
8. J. Cheng, D. A. Bell, and W. Liu. Learning belief networks from data: An information theory based approach. In Proc. CIKM, pages 263–274, 1997.
9. T. M. Cover and J. A. Thomas. Elements of Information Theory. Wiley, 1991.
10. T. Dasu, T. Johnson, S. Muthukrishnan, and V. Shkapenyuk. Mining database structure; or, how to build a data quality browser. In Proc. SIGMOD, pages 240–251, 2002.
11. A. Deshpande, M. Garofalakis, and R. Rastogi. Independence is good: Dependency-based histogram synopses for high-dimensional data. In Proc. ACM SIGMOD, pages 199–210, 2001.
12. L. Getoor, T. B, and K. D. Selectivity estimation using probabilistic models. In Proc. ACM SIGMOD, 2001.
13. J. Gray, S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M.Venkatrao, F. Pellow, and H. Pirahesh. Data cube: A relational aggregation operator generalizsing group-by, cross-tab, and sub-totals. J. Data Mining and Knowledge Discovery, 1(1):29–53, 1997.
14. D. Gunopulos, R. Khardon, H. Mannila, S. Saluja, H. Toivonen, and R. S. Sharma. Discovering all most specific sentences. ACM Trans. Database Syst., 28(2):140–174, 2003.
15. P. J. Haas and P. G. Brown. BHUNT: Automatic discovery of fuzzy algebraic constraints in relational data. In Proc. VLDB, pages 668–679, 2003.
16. P. J. Haas and L. Stokes. Estimating the number of classes in a finite population. J. Amer. Statist. Assoc., 93:1475–1487, 1998.
17. Y. Huhtala, J. Kärkkäinen, P. Porkka, and H. Tiovonen. TANE: An efficient algorithm for discovering functional and approximate dependencies. Comput. J., 42:100–111, 1999.
18. IBM Research. Autonomic computing. In http://www.research.ibm.com/autonomic, 2003.
19. I. Ilyas, V. Markl, P. J. Haas, P. G. Brown, and A. Aboulnaga. CORDS: Automatic generation of correlation statistics in DB2. In Proc. VLDB, pages 1341–1344, 2004.
20. Y. E. Ioannidis. The history of histograms (abridged). In Proc. VLDB, pages 19–30, 2003.
21. J. Kivinen and H. Mannila. Approximate dependency inference from relations. Theoret. Comput. Sci., 149:129–149, 1995.
22. Y. Matias, J. S. Vitter, and M. Wang. Wavelet-Based histograms for selectivity estimation. In Proc. ACM SIGMOD, pages 448–459, 1998.
23. Microsoft Research. The Autoadmin project. In http://research.microsoft.com/autoadmin, 2003.
24. J.-M. Petit, F. Toumani, J.-F. Boulicaut, and J. Kouloumdjian. Towards the reverse engineering of denormalized relational databases. In Proc. ICDE, pages 218–227, 1996.
25. M. Siegelan, E. Sciore, and S. Salveter. A method for automatic rule derivation to support semantic query optimization. ACM Trans. Database Sys., 17:563–600, 1992.
26. Y. Sismanis, A. Deligiannakis, N. Roussopoulos, and Y. Kotidis. Dwarf: Shrinking the Petacube. In Proc. ACM SIGMOD, 2002.
27. Y. Sismanis and N. Roussopoulos. The Polynomial Complexity of Fully Materialized Coalesced Cubes. In Proc. VLDB, 2004.
28. R. Srikant and R. Agrawal. Mining quantitative associations rules in large relational tables. In Proc. ACM SIGMOD, pages 1–12, 1996.
29. U. Srivastava, P. J. Haas, V. Markl, and N. Megiddo. ISOMER: Consistent histogram construction using query feedback. In Proc. ICDE, 2006.
30. M. Stillger, G. Lohman, V. Markl, and M. Kandil. LEO - DB2’s LEarning Optimizer. In Proc. VLDB, pages 19–28, 2001.
31. http://www.tpc.org/tpch/default.asp.
32. G. Weikum, A. Moenkeberg, C. Hasse, and P. Zabback. Self-tuning database technology and information services: from wishful thinking to viable engineering. In Proc. VLDB, pages 20–34, 2002.
33. S. K. M. Wong, C. J. Butz, and Y. Xiang. Automated database schema design using mined data dependencies. J. Amer. Soc. Inform. Sci., 49:455–470, 1998.