Massive datasets

Wiley Interdisciplinary Reviews: Computational Statistics

Volumn 1, Issue 1, 2009, Pages 25-32

  Kettenring, Jon R ^a  

^a DREW UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BACKGROUND INFORMATION; DATA SETS; INTERDISCIPLINARY TEAMWORK; MASSIVE DATA SETS; STANDARD STATISTICAL ANALYSIS; STATISTICAL STRATEGIES;

EID: 78651583275     PISSN: 19395108     EISSN: 19390068     Source Type: Journal    
DOI: 10.1002/wics.15     Document Type: Review

References (43)

1. Granger CWJ. Extracting information from megapanels and high-frequency data. Stat Neerl 1998, 52: 258-272.
2. Unwin A. Introduction. In: Unwin A, Theus M, Hofmann H, eds. Graphics of Large Datasets. New York: Springer; 2006, 1-27.
3. Mallows CL. Robust methods-some examples of their use. Amer Statist 1979, 33: 179-184.
4. 4. Friedman JH. The role of statistics in the data revolution? Int Statist Rev 2001, 69: 5-10. (Pubitemid 33255448)
5. 5. Yu B. Embracing statistical challenges in the information technology age. Technometrics 2007, 49: 237-248. (Pubitemid 47292483)
6. Committee on Applied and Theoretical Statistics, National Research Council. Massive data sets. Proceedings of a Workshop. Washington, DC: National Academy Press; 1996.
7. Buja A, Keller-McNulty S. Introduction to the special section on massive datasets. J Comp Graph Statist 1999, 8: 544.
8. Committee on Applied and Theoretical Statistics, National Research Council. Statistical analysis of massive data streams. Proceedings of a Workshop. Washington, DC: National Academy Press; 2004.
9. Huber PJ. Massive datasets workshop: four years after. J Comp Graph Statist 1999, 8: 635-652.
10. Wegman EJ. Huge data sets and the frontiers of computational feasibility. J Comp Graph Statist 1995, 4: 281-295.
11. st century. J Amer Statist Assoc 1997, 92: 1229-1234.
12. Braverman A. Understanding large-scale structure in massive data sets. In: Melnick E, Everitt B, eds. Encyclopedia of Quantitative Risk Assessment. Chichester, UK: Wiley; 2008.
13. Bolton RJ, Hand D. Statistical fraud detection: a review. Statist Sci 2002, 17: 235-255.
14. 14. Duffield N. Sampling for passive internet measurement: a review. Statist Sci 2004, 19: 472-498. (Pubitemid 40472647)
15. 15. Kötter R. Neuroscience databases: tools for exploring brain structure-function relationships. Phil Trans R Soc Lond B 2001, 356: 1111-1120. (Pubitemid 32882032)
16. Eddy WF, Fitzgerald M., Genovese C, Lazar N, Mockus A, et al. The challenge of functional magnetic resonance imaging. J Comp Graph Statist 1999, 8: 545-558.
17. 17. Knuteson B, Padley P. Statistical challenges with massive datasets in particle physics. J Comp Graph Statist 2003, 12: 808-828. (Pubitemid 38148590)
18. Committee on Applied and Theoretical Statistics, Dumais S. Information retrieval: finding needles in massive haystacks. Massive Data Sets. Proceedings of a workshop. Washington, DC: National Academy Press; 1996, 23-32.
19. 19. Li Y, Shawe-Taylor J. Advanced learning algorithms for cross-language patent retrieval and classification. Inf Process Manag 2007, 43: 1183-1199. (Pubitemid 46636026)
20. Brunner RJ, Djorgovski SG, Prince TA, Szalay AS. Massive datasets in astronomy. In: Abello J, Pardalos PM, Resende MGC, eds. Handbook of Massive Data Sets. Boston: Kluwer; 2002, 931-979.
21. Kamath C. Mining the sky: data analysis meets astronomy. SIAM News 2002, 35(3): 1, 8-9.
22. 22. Balasubramanian S, Gupta S, Kamakura W, Wedel M. Modeling large data sets in marketing. Stat Neerl 1998, 52: 303-323. (Pubitemid 128359360)
23. Cook D, Swayne DF. Interactive and Dynamic Graphics for Data Analysis. New York: Springer; 2007.
24. Hurley CB. Clustering visualizations of multidimensional data. J Comp Graph Statist 2004, 13: 788-806.
25. Tukey PA, Tukey JW. Data-driven view selection; agglomeration and sharpening. In: Barnett V, ed. Interpreting Multivariate Data. Chichester, UK: Wiley; 1981, 215-243.
26. Eick SG, Karr AF. Visual scalability. J Comp Graph Statist 2002, 11: 22-43.
27. Unwin A, Theus M. Graphics of a large dataset. In: Unwin A, Theus M, Hofmann H, eds. Graphics of Large Datasets. New York: Springer; 2006, 227-249.
28. th ACM SIGKDD. Association for Computing Machinery, NewYork, 1999, 6-15.
29. DuMouchel W. Data squashing: constructing summary data sets. In: Abello J, Pardalos PM, Resende MGC, eds. Handbook of Massive Data Sets. Boston: Kluwer; 2002, 579-591.
30. Li X. Data reduction via adaptive sampling. Communications in Information and System 2002, 2: 53-68.
31. Braverman A. Compressing massive geophysical datasets using vector quantization. J Comp Graph Statist 2002, 11: 44-62.
32. Kettenring JR. The practice of cluster analysis. J Classif 2006, 23: 3-30.
33. Bradley P, Fayyad U, Reina C. Scaling clustering algorithms to large databases. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD98). Menlo Park, CA: AAAI Press; 1998, 9-15.
34. Littau D, Boley D. Clustering very large data sets with principal direction divisive partitioning. In: Kogan J, Nicholas C, Teboulle M, eds. Grouping Multidimensional Data: Recent Advances in Clustering. New York: Springer; 2006, 99-126.
35. Maitra R. Clustering massive datasets with applications in software metrics and tomography. Technometrics 2001, 43: 336-346.
36. Huber PJ. Approximate models. In: Huber-Carol C, Balakrishnan N, Nikulin MS, Mesbah M, eds. Goodness-of-Fit Tests and Model Validity. Boston: Birkhauser; 2002, 25-41.
37. Bickel PJ, Li B. Regularization in statistics. Test 2006, 15: 271-344.
38. Zoi H, Hastie T, Tibshirani R. Sparse principal component analysis. J Comp Graph Statist 2006, 15: 265-286.
39. Balakrishnan S, Madigan D. Algorithms for sparse linear classifiers in the massive data setting. J Mach Learn Res 2008, 9: 313-337.
40. Lindsay B, Kettenring J, Siegmund DO. A report on the future of statistics. Statist Sci 2004, 19: 387-413.
41. Fayyad UM, Smyth P. Cataloging and mining massive datasets for science data analysis. J Comp Graph Statist 1999; 8; 589-610.
42. Khumbah N-A, Wegman E. Data compression by geometric quantization. In: Akritas MG, Politis DN, eds. Recent Advances and Trends in Nonparametric Statistics. Amsterdam: Elsevier; 2003, 35-48.
43. Lambert D. What use is statistics for massive data? In: Lecture Notes-Monograph Series 43, Crossing Boundaries: Statistical Essays in Honor of Jack Hall. Bethesda, MD: Institute of Mathematical Statistics; 2003, 217-228.