SEEDB: Efficient data-driven visualization recommendations to support visual analytics

Proceedings of the VLDB Endowment

Volumn 8, Issue 13 13, 2015, Pages 2182-2193

  Vartak, Manasi ^a   Rahman, Sajjadur ^b   Madden, Samuel ^a   Parameswaran, Aditya ^b   Polyzotis, Neoklis ^c  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^c GOOGLE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA VISUALIZATION; MIDDLEWARE;

APPROPRIATE METRICS; DATA ANALYSTS; DATA DRIVEN; HIGH-DIMENSIONAL DATASET; INTERESTINGNESS; OPTIMISATIONS; TIME-SCALES; VISUAL ANALYSIS; VISUAL ANALYTICS; WORK-FLOWS;

VISUALIZATION;

EID: 84952767080     PISSN: None     EISSN: 21508097     Source Type: Journal    
DOI: 10.14778/2831360.2831371     Document Type: Chapter

References (40)

1. Tableau public, www.tableaupublic.com. [Online; accessed 3-March-2014].
2. S. Agarwal, R. Agrawal, P. Deshpande, A. Gupta, J. F. Naughton, R. Ramakrishnan, and S. Sarawagi. On the computation of multidimensional aggregates. VLDB '96, pages 506-521, 1996.
3. C. Ahlberg. Spotfire: An information exploration environment. SIGMOD Rec., 25(4):25-29, Dec. 1996.
4. J.-Y. Audibert, S. Bubeck, et al. Best arm identification in multi-armed bandits. COLT 2010-Proceedings, 2010.
5. S. Bubeck, T. Wang, and N. Viswanathan. Multiple identifications in multi-armed bandits. In Proceedings of the Thirtieth International Conference on Machine Learning, JMLR '13, 2013.
6. P. R. Doshi, E. A. Rundensteiner, and M. O. Ward. Prefetching for visual data exploration. In DASFAA 2003, pages 195-202. IEEE, 2003.
7. P. M. Fernandez. Red brick warehouse: A read-mostly rdbms for open smp platforms. SIGMOD Rec., 23(2):492-, May 1994.
8. H. Gonzalez et al. Google fusion tables: web-centered data management and collaboration. In SIGMOD Conference, pages 1061-1066, 2010.
9. J. Gray, S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M. Venkatrao, F. Pellow, and H. Pirahesh. Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub-totals. Data Min. Knowl. Discov., 1(1):29-53, Jan. 1997.
10. V. Harinarayan, A. Rajaraman, and J. D. Ullman. Implementing data cubes efficiently. SIGMOD '96, pages 205-216, 1996.
11. W. Hoeffding. Probability inequalities for sums of bounded random variables. Journal of the American statistical association, 58(301):13-30, 1963.
12. E. Horvitz. Principles of mixed-initiative user interfaces. CHI'99, pages 159-166. ACM, 1999.
13. I. F. Ilyas, W. G. Aref, and A. K. Elmagarmid. Supporting top-k join queries in relational databases. The VLDB Journal, 13(3):207-221, Sept. 2004.
14. D. S. Johnson, A. Demers, J. D. Ullman, M. R. Garey, and R. L. Graham. Worst-case performance bounds for simple one-dimensional packing algorithms. SIAM Journal on Computing, 3(4):299-325, 1974.
15. S. Kandel et al. Profiler: integrated statistical analysis and visualization for data quality assessment. In AVI, pages 547-554, 2012.
16. A. Key, B. Howe, D. Perry, and C. Aragon. Vizdeck: Self-organizing dashboards for visual analytics. SIGMOD '12, pages 681-684, 2012.
17. A. Kim, E. Blais, A. G. Parameswaran, P. Indyk, S. Madden, and R. Rubinfeld. Rapid sampling for visualizations with ordering guarantees. CoRR, abs/1412.3040, 2014.
18. M. Garey et. al. Computers and Intractability; A Guide to the Theory of NP-Completeness. W. H. Freeman & Co., 1990.
19. J. Mackinlay. Automating the design of graphical presentations of relational information. ACM Trans. Graph., 5(2):110-141, Apr. 1986.
20. J. D. Mackinlay et al. Show me: Automatic presentation for visual analysis. IEEE Trans. Vis. Comput. Graph., 13(6):1137-1144, 2007.
21. P. Marcel and E. Negre. A survey of query recommendation techniques for data warehouse exploration. In EDA, pages 119-134, 2011.
22. K. Morton, M. Balazinska, D. Grossman, and J. D. Mackinlay. Support the data enthusiast: Challenges for next-generation data-analysis systems. PVLDB, 7(6):453-456, 2014.
23. C. Ordonez and Z. Chen. Exploration and visualization of olap cubes with statistical tests. VAKD '09, pages 46-55, 2009.
24. A. Parameswaran, N. Polyzotis, and H. Garcia-Molina. Seedb: Visualizing database queries efficiently. PVLDB, 7(4), 2013.
25. J. Polowinski and M. Voigt. Viso: a shared, formal knowledge base as a foundation for semi-automatic infovis systems. In CHI'13 Extended Abstracts on Human Factors in Computing Systems, pages 1791-1796. ACM, 2013.
26. Postgresql.org. Postgresql: Number of database connections, 2014. [Online; accessed 20-May-2014].
27. C. Re, N. Dalvi, and D. Suciu. Efficient top-k query evaluation on probabilistic data. In in ICDE, pages 886-895, 2007.
28. U. M. L. Repository. Uci machine learning repository, 2015. [Online; accessed 29-June-2015].
29. S. Sarawagi. Explaining differences in multidimensional aggregates. In VLDB, pages 42-53, 1999.
30. S. Sarawagi. User-adaptive exploration of multidimensional data. In VLDB, pages 307-316, 2000.
31. S. Sarawagi, R. Agrawal, and N. Megiddo. Discovery-driven exploration of olap data cubes. EDBT '98, pages 168-182, 1998.
32. G. Sathe and S. Sarawagi. Intelligent rollups in multidimensional olap data. In VLDB, pages 531-540, 2001.
33. T. K. Sellis. Multiple-query optimization. ACM TODS, 13(1):23-52, 1988.
34. R. J. Serfling et al. Probability inequalities for the sum in sampling without replacement. The Annals of Statistics, 2(1):39-48, 1974.
35. C. Stolte et al. Polaris: a system for query, analysis, and visualization of multidimensional databases. CACM, 51(11):75-84, 2008.
36. M. Vartak, S. Madden, A. G. Parameswaran, and N. Polyzotis. SEEDB:automatically generating query visualizations. PVLDB, 7(13):1581-1584, 2014.
37. M. Vartak, S. Rahman, S. Madden, A. G. Parameswaran, and N. Polyzotis. Seedb: Efficient data-driven visualization recommendations to support visual analytics. Technical Report, data-people.cs.illinois.edu/seedb-tr.pdf.
38. J. Vermorel and M. Mohri. Multi-armed bandit algorithms and empirical evaluation. In ECML, pages 437-448, 2005.
39. K. Wongsuphasawat, D. Moritz, A. Anand, J. Mackinlay, B. Howe, and J. Heer. Voyager: Exploratory analysis via faceted browsing of visualization recommendations. IEEE Trans. Visualization & Comp. Graphics, 2015.
40. E. Wu and S. Madden. Scorpion: Explaining away outliers in aggregate queries. Proc. VLDB Endow., 6(8):553-564, June 2013.