A blackbox approach to query cardinality estimation

CIDR 2007 - 3rd Biennial Conference on Innovative Data Systems Research

Volumn , Issue , 2007, Pages 56-67

  Malik, Tanu ^a   Burns, Randal ^a   Chawla, Nitesh ^a  

^a Johns Hopkins University   (United States)

Author keywords

Design; Performance

Indexed keywords

BLACK BOX APPROACH; BLACK BOXES; CACHING MECHANISM; CARDINALITIES; CARDINALITY ESTIMATIONS; COMPUTATION RESOURCES; DATA DISTRIBUTION; DATA SOURCE; FEDERATED QUERIES; HIGH-DIMENSIONAL; INPUT SPACE; PERFORMANCE; QUERY EXECUTION PLAN; RESOURCE OPTIMIZATION;

DESIGN; ESTIMATION; MULTIVARIABLE CONTROL SYSTEMS; QUERY PROCESSING; RESEARCH;

SEARCH ENGINES;

EID: 52649153887     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (31)

1. A. Aboulnaga and S. Chaudhuri. Self-tuning histograms: Building histograms without looking at data. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 181-192, 1999. (Pubitemid 129597334)
2. J. L. Ambite and C. A. Knoblock. Flexible and scalable query planning in distributed and heterogeneous environments. In Conference on Artificial Intelligence Planning Systems, pages 3-10, 1998.
3. K. Amiri, S. Park, R. Tewari, and S. Padmanabhan. DBProxy: A dynamic data cache for Web applications. In International Conference of Data Engineering, page 821, Los Alamitos, CA, USA, 2003. IEEE Computer Society.
4. K. Amiri, S. Park, R. Tewari, and S. Padmanabhan. Scalable template-based query containment checking for Web semantic caches. In Proceedings of the International Conference on Data Engineering, pages 493-504, 2003.
5. A. Bagchi, A. Chaudhary, M. T. Goodrich, C. Li, and M. Shmueli-Scheuer. Achieving communication efficiency through push-pull partitioning of semantic spaces in client-server architectures. IEEE Transactions on Knowledge and Data Engineering (TKDE), September 2006.
6. D. Barbará, W. DuMouchel, C. Faloutsos, P. J. Haas, J. M. Hellerstein, Y. E. Ioannidis, H. V. Jagadish, T. Johnson, R. T. Ng, V. Poosala, K. A. Ross, and K. C. Sevcik. The New Jersey Data Reduction Report. IEEE Data Engineering Bulletin, 20(4):3-45, 1997.
7. N. Bruno and S. Chaudhuri. Exploiting statistics on query expressions for optimization. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 263-274, 2002. (Pubitemid 34985553)
8. N. Bruno, S. Chaudhuri, and L. Gravano. STHoles: A multidimensional workload-aware histogram. In Proceedings of the ACM SIGMOD International Conference on Management of Data, 2001.
9. C. M. Chen and N. Roussopoulos. Adaptive selectivity estimation using query feedback. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 161-172, 1994.
10. I. Foster and C. Kesselman. Globus: A metacomputing infrastructure toolkit. The International Journal of Supercomputer Applications and High Performance Computing, 11(2):115-128, Summer 1997. (Pubitemid 127673968)
11. J. Gray, D. T. Liu, M. Nieto-Santisteban, A. Szalay, D. J. DeWitt, and G. Heber. Scientific data management in the coming decade. SIGMOD Record, 34(4):34-41, 2005. (Pubitemid 43152546)
12. G. Hamerly and C. Elkan. Learning the k in k-means. In Neural Information Processing Systems, 2003.
13. Z. He, B. S. Lee, and R. Snapp. Self-tuning cost modeling of user-defined functions in an object-relational DBMS. ACM Transanctions on Database Systems, 30(3):812-853, 2005. (Pubitemid 43906384)
14. L. Lim, M. Wang, and J. S. Vitter. CXHist : An on-line classification-based histogram for XML string selectivity estimation. In Proceedings of the International Conference on Very Large Data Bases, pages 1187-1198, 2005. (Pubitemid 43991088)
15. M. Litzkow, M. Livny, and M. Mutka. Condor - A hunter of idle workstations. In Proceedings of the 8th International Conference of Distributed Computing Systems, June 1988.
16. D. T. Liu and M. J. Franklin. The design of GridDB: A Data-centric overlay for the scientific grid. In VLDB, pages 600-611, 2004.
17. T. Malik, R. Burns, N. Chawla, and A. Szalay. Estimating query result sizes for proxy caching in scientific database federations. In SuperComputing, April 2006.
18. T. Malik, R. C. Burns, and A. Chaudhary. Bypass caching: Making scientific databases good network citizens. In Intl'Conference on Data Engineering, pages 94-105, 2005. (Pubitemid 41731118)
19. T. Malik, A. S. Szalay, A. S. Budavri, and A. R. Thakar. SkyQuery: A WebService Approach to Federate Databases. In Proceedings of the Conference on Innovative Data Systems Research, January 2003.
20. T. Mitchell. Machine Learning. McGraw Hill, 1997.
21. Statistics in MSSQL Server 2005, http://www.microsoft.com/technet/ prodtechnol/sql/2005/qrystats.mspx.
22. C. Olston, B. Loo, and J. Widom. Adaptive precision setting for cached approximate values. In ACM SIGMOD, pages 355-366, 2001.
23. C. Olston and J. Widom. Best-effort cache synchronization with source cooperation. In ACM SIGMOD, pages 73-84, 2002. (Pubitemid 34985537)
24. D. Pelleg and A. Moore. X-means: Extending k-means with efficient estimation of the number of clusters. In Proceedings of the Seventeenth International Conference on Machine Learning, pages 727-734, San Francisco, 2000. Morgan Kaufmann.
25. J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1993.
26. L. Serafini, H. Stockinger, K. Stockinger, and F. Zini. Agent-based query optimisation in a grid environment. In International Conference on Applied Informatics, February 2001.
27. A. P. Sheth and J. A. Larson. Federated database systems for managing distributed, heterogeneous, and autonomous databases. ACM Computing Surveys, 22(3):183-236, 1990.
28. Open SkyQuery, http://www.openskyquery.net, 2000.
29. U. Srivastava, P. J. Haas, V. Markl, M. Kutsch, and T. M. Tran. Isomer: Consistent histogram construction using query feedback. In 22nd International Conference on Data Engineering, page 39, Washington, DC, USA, 2006. IEEE Computer Society. (Pubitemid 44539831)
30. M. Stillger, G. M. Lohman, V. Markl, and M. Kandil. LEO - DB2's LEarning Optimizer. In Proceedings of the International Conference on Very Large Data Bases, pages 19-28, 2001.
31. I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2005.