Join Synopses for Approximate Query Answering

SIGMOD Record (ACM Special Interest Group on Management of Data)

Volumn 28, Issue 2, 1999, Pages 275-286

  Acharya, Swarup ^a   Gibbons, Phillip B ^a   Poosala, Viswanath ^a   Ramaswamy, Sridhar ^a,b  

^a LUCENT TECHNOLOGIES   (United States)

^b Epiphany Inc   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0345982315     PISSN: 01635808     EISSN: None     Source Type: Journal    
DOI: 10.1145/304181.304207     Document Type: Article

References (27)

1. S. Acharya, P. B. Gibbons, V. Poosala, and S. Ramaswamy. The Aqua approximate query answering system. In Proc. ACM SIGMOD International Conf. on Management of Data, June 1999. Demonstration paper.
2. S. Acharya, P. B. Gibbons, V. Poosala, and S. Ramaswamy. Join synopses for approximate query answering. Technical report, Bell Laboratories, Murray Hill, New Jersey, 1999. Full version of the paper appearing in SIGMOD'99.
3. N. Alon, Y. Matias, and M. Szegedy. The space complexity of approximating the frequency moments. In Proc. 28th ACM Symp. on the Theory of Computing, pages 20-29, May 1996. Full version to appear in JCSS special issue for STOC'96.
4. S. Acharya, V. Poosala, and S. Ramaswamy. Selectivity estimation in spatial databases. In Proc. ACM SIGMOD International Conf. on Management of Data, June 1999.
5. D. Barbara, W. DuMouchel, C. Faloutsos, P. J. Haas, J. M. Hellerstein, Y. Ioannidis, H. V. Jagadish, T. Johnson, R. Ng, V. Poosala, K. A. Ross, and K. C. Sevcik. The New Jersey data reduction report. Bulletin of the Technical Committee on Data Engineering, 20(4):3-45, 1997.
6. C. M. Chen and N. Roussopoulos. Adaptive selectivity estimation using query feedback. In Proc. ACM SIGMOD International Conf. on Management of Data, pages 161-172, May 1994.
7. S. Ganguly, P. B. Gibbons, Y. Matias, and A. Silberschatz. Bifocal sampling for skew-resistant join size estimation. In Proc. ACM SIGMOD International Conf. on Management of Data, pages 271-281, June 1996.
8. P. B. Gibbons and Y. Matias. New sampling-based summary statistics for improving approximate query answers. In Proc. ACM SIGMOD International Conf. on Management of Data, pages 331-342, June 1998.
9. P. B. Gibbons and Y. Matias. Selecting estimation procedures and bounds for approximate answering of aggregation queries. Technical report. Bell Laboratories, Murray Hill New Jersey, 1999.
10. P. B. Gibbons and Y. Matias. Synopsis data structures for massive data sets. DIMACS: Series in Discrete Mathematics and Theoretical Computer Science, 1999. To appear. Available as Bell Labs tech. rep., Sept. 1998, and at http://www.bell-labs.com/∼pbgibbons/.
11. P. B. Gibbons, Y. Matias, and V. Poosala. Aqua project white paper. Technical report, Bell Laboratories, Murray Hill, New Jersey, December 1997.
12. P. B. Gibbons, Y. Matias, and V. Poosala. Fast incremental maintenance of approximate histograms. In Proc. 23rd International Conf. on Very Large Data Bases, pages 466-475, August 1997.
13. P. J. Haas. Hoeffding inequalities for join-selectivity estimation and online aggregation. Technical Report RJ 10040, IBM Almaden Research Center, San Jose, CA, 1996.
14. P. J. Haas. Large-sample and deterministic confidence intervals for online aggregation. In Proc. 9th International Conf. on Scientific and Statistical Database Management, August 1997.
15. J. M. Hellerstein, P. J. Haas, and H. J. Wang. Online aggregation. In Proc. ACM SIGMOD International Conf. on Management of Data, pages 171-182, May 1997.
16. P. J. Haas, J. F. Naughton, and A. N. Swami. On the relative cost of sampling for join selectivity estimation. In Proc. 13th ACM Symp. on Principles of Database Systems, pages 14-24, May 1994.
17. P. J. Haas, J. F. Naughton, S. Seshadri, and L. Stokes. Sampling-based estimation of the number of distinct values of an attribute. In Proc. 21st International Conf. on Very Large Data Bases, pages 311-322, September 1995.
18. W.-C. Hou, G. Özsoyoǧlu, and B. K. Taneja. Statistical estimators for relational algebra expressions. In Proc. 7th ACM Symp. on Principles of Database Systems, pages 276-287, March 1988.
19. R. P. Kooi. The Optimization of Queries in Relational Databases. PhD thesis, Case Western Reserve University, September 1980.
20. R. J. Lipton and J. F. Naughton. Query size estimation by adaptive sampling. J. Computer and System Sciences, 51(1):18-25, 1995.
21. R. J. Lipton, J. F. Naughton, and D. A. Schneider. Practical selectivity estimation through adaptive sampling. In Proc. ACM SIGMOD International Conf. on Management of Data, pages 1-12, May 1990.
22. F. Olken and D. Rotem. Maintenance of materialized views of sampling queries. In Proc. 8th IEEE International Conf. on Data Engineering, pages 632-641, February 1992.
23. V. Poosala, Y. E. Ioannidis, P. J. Haas, and E. J. Shekita. Improved histograms for selectivity estimation of range predicates. In Proc. ACM SIGMOD International Conf. on Management of Data, pages 294-305, June 1996.
24. V. Poosala. Histogram-based estimation techniques in databases. PhD thesis, Univ. of Wisconsin-Madison, 1997.
25. P. G. Selinger, M. M. Astrahan, D. D. Chamberlin, R. A. Lorie, and T. T. Price. Access path selection in a relational database management system. In Proc. ACM SIGMOD International Conf. on Management of Data, pages 23-34, June 1979.
26. D. Schneider. The ins & outs (and everything in between) of data warehousing. Tutorial in the 23rd International Conf. on Very Large Data Bases, August 1997.
27. S. V. Vrbsky and J. W. S. Liu. Approximate - a query processor that produces monotonically improving approximate answers. IEEE Trans. on Knowledge and Data Engineering, 5(6):1056-1068, 1993.