The analytical bootstrap: A new method for fast error estimation in Approximate Query Processing

Proceedings of the ACM SIGMOD International Conference on Management of Data

Volumn , Issue , 2014, Pages 277-288

  Zeng, Kai ^a   Gao, Shi ^a   Mozafari, Barzan ^b   Zaniolo, Carlo ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF MICHIGAN   (United States)

Author keywords

Approximate query processing; Bootstrap; Error estimation

Indexed keywords

ALGORITHMS; ERROR ANALYSIS; PROBABILITY DISTRIBUTIONS; QUERY PROCESSING; SEMANTICS;

APPROXIMATE QUERY PROCESSING; APPROXIMATION RESULTS; BOOTSTRAP; COMPUTATIONAL OVERHEADS; ERROR QUANTIFICATION; PROBABILISTIC FRAMEWORK; PROBABILISTIC RELATIONAL MODELS; TRADITIONAL APPROACHES;

QUERY LANGUAGES;

EID: 84904301645     PISSN: 07308078     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2588555.2588579     Document Type: Conference Paper

References (45)

1. Conviva Inc. http://www.conviva.com/.
2. MonetDB. http://www.monetdb.org/Home.
3. The R Project. http://www.r-project.org/.
4. TPC-H Benchmark. http://www.tpc.org/tpch/.
5. Vertica Inc. http://www.vertica.com/.
6. S. Acharya, P. B. Gibbons, et al. Join Synopses for Approximate Query Answering. In SIGMOD, pages 275-286, 1999.
7. S. Acharya, P. B. Gibbons, et al. The Aqua Approximate Query Answering System. In SIGMOD, pages 574-576, 1999.
8. S. Agarwal, H. Milner, et al. Knowing When You're Wrong: Building Fast and Reliable Approximate Query Processing Systems. In SIGMOD, 2014.
9. S. Agarwal, B. Mozafari, et al. BlinkDB: Queries with Bounded Errors and Bounded Response Times on Very Large Data. In EuroSys, pages 29-42, 2013.
10. L. Antova, T. Jansen, et al. Fast and Simple Relational Processing of Uncertain Data. In ICDE, pages 983-992, 2008.
11. B. Babcock, S. Chaudhuri, et al. Dynamic Sample Selection for Approximate Query Processing. In SIGMOD, pages 539-550, 2003.
12. B. Babcock, M. Datar, et al. Load Shedding for Aggregation Queries over Data Streams. In ICDE, page 350, 2004.
13. P. J. Bickel and D. A. Freedman. Some Asymptotic Theory for the Bootstrap. The Annals of Statistics, 9(6):1196-1217, 1981.
14. G. Box, J. S. Hunter, et al. Statistics for Experimenters: Design, Innovation, Discovery. Wiley-Interscience, 2005.
15. M. Charikar, S. Chaudhuri, et al. Towards Estimation Error Guarantees for Distinct Values. In PODS, pages 268-279, 2000.
16. S. Chaudhuri, G. Das, et al. Optimized stratified sampling for approximate query processing. TODS, 32(2):9, 2007.
17. Y. Cui, J. Widom, et al. Tracing the lineage of view data in a warehousing environment. TODS, 25(2):179-227, 2000.
18. N. N. Dalvi and D. Suciu. Efficient query evaluation on probabilistic databases. VLDBJ, 16:523-544, 2007.
19. B. Efron and R. J. Tibshirani. An Introduction to the Bootstrap. Chapman & Hall, New York, 1993.
20. H. Garcia-Molina, J. D. Ullman, et al. Database systems - the complete book (2. ed.). Pearson Education, 2009.
21. B. Glavic and G. Alonso. Perm: Processing Provenance and Data on the Same Data Model through Query Rewriting. In ICDE, pages 174-185, 2009.
22. J. M. Hellerstein, P. J. Haas, et al. Online Aggregation. In SIGMOD, pages 171-182, 1997.
23. Y. Hu, S. Sundara, et al. Estimating Aggregates in Time-Constrained Approximate Queries in Oracle. In EDBT, pages 1104-1107, 2009.
24. C. Jermaine, S. Arumugam, et al. Scalable Approximate Query Processing with DBO Engine. In SIGMOD, pages 1-54, 2007.
25. S. Joshi and C. Jermaine. Sampling-Based Estimators for Subset-Based Queries. VLDB J., 18(1):181-202, 2009.
26. G. Karvounarakis and T. J. Green. Semiring-Annotated Data: Queries and Provenance? SIGMOD Record, 41(3):5-14, 2012.
27. A. Kleiner, A. Talwalkar, et al. A General Bootstrap Performance Diagnostic. In KDD, pages 419-427, 2013.
28. A. Kleiner, A. Talwalkar, et al. The Big Data Bootstrap. In ICML, 2012.
29. P. G. Kolaitis and M. Y. Vardi. Conjunctive-Query Containment and Constraint Satisfaction. In PODS, pages 205-213, 1998.
30. N. Laptev, K. Zeng, et al. Early Accurate Results for Advanced Analytics on MapReduce. PVLDB, 5(10):1028-1039, 2012.
31. B. Mozafari and C. Zaniolo. Optimal Load Shedding with Aggregates and Mining Queries. In ICDE, pages 76-88, 2010.
32. C. Olston, E. Bortnikov, et al. Interactive Analysis of Web-Scale Data. In CIDR, 2009.
33. N. Pansare, V. R. Borkar, et al. Online Aggregation for Large MapReduce Jobs. PVLDB, 4(11):1135-1145, 2011.
34. A. Pol and C. Jermaine. Relational Confidence Bounds Are Easy With The Bootstrap. In SIGMOD, pages 587-598, 2005.
35. T. Rabl, M. Poess, et al. Variations of the Star Schema Benchmark to Test the Effects of Data Skew on Query Performance. In SPEC, pages 361-372, 2013.
36. C. Ré and D. Suciu. The Trichotomy of HAVING Queries on a Probabilistic Database. VLDBJ, 18(5):1091-1116, 2009.
37. P. Sen, A. Deshpande, et al. Read-Once Functions and Query Evaluation in Probabilistic Databases. PVLDB, 3(1):1068-1079, 2010.
38. M. M. Siddiqui and C. Butler. Asymptotic Joint Distribution of Linear Systematic Statistics from Multivariate Distributions. Journal of the American Statistical Association, 64(325):300-305, 1969.
39. T. T. L. Tran, Y. Diao, et al. Supporting User-Defined Functions on Uncertain Data. PVLDB, 6(6):469-480, 2013.
40. T. T. L. Tran, L. Peng, et al. CLARO: Modeling and Processing Uncertain Data Streams. VLDBJ, (5):651-676, 2012.
41. A. van der Vaart and J. Wellner. Weak Convergence and Empirical Processes. Springer, corrected edition, Nov. 2000.
42. D. Z. Wang, E. Michelakis, et al. Bayesstore: Managing large, uncertain data repositories with probabilistic graphical models. PVLDB, 1(1):340-351, 2008.
43. S. Wilhelm. tmvtnorm: A Package for the Truncated Multivariate Normal Distribution. sigma, 2:2.
44. S. Wu, B. C. Ooi, et al. Continuous Sampling for Online Aggregation over Multiple Queries. In SIGMOD, pages 651-662, 2010.
45. K. Zeng, S. Gao, et al. The Analytical Bootstrap: A New Method for Fast Error Estimation in Approximate Query Processing. Technical Report CSD #130028, UCLA, 2013.