Crowd-powered find algorithms

Proceedings - International Conference on Data Engineering

Volumn , Issue , 2014, Pages 964-975

  Das Sarma, Anish ^a   Parameswaran, Aditya ^b   Garcia Molina, Hector ^c   Halevy, Alon ^d  

^a ClearList Inc   (United States)

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

^c Stanford University   (United States)

^d GOOGLE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA PROCESSING; DESIGN;

CANDIDATE SETS; DATA SET; ERROR PRONES; EXPECTED COSTS; MONETARY COSTS; OPTIMAL ALGORITHM;

COSTS;

EID: 84901813373     PISSN: 10844627     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDE.2014.6816715     Document Type: Conference Paper

References (37)

1. F. N. Afrati, A. D. Sarma, D. Menestrina, A. Parameswaran, and J. D. Ullman. Fuzzy joins using mapreduce. In ICDE, 2012.
2. A. Arasu, M. Götz, and R. Kaushik. On active learning of record matching packages. In SIGMOD Conference, pages 783-794, 2010.
3. D. P. Bertsekas and J. N. Tsitsiklis. Parallel and distributed computation: numerical methods. Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1989.
4. D. Culler, R. Karp, D. Patterson, A. Sahay, K. E. Schauser, E. Santos, R. Subramonian, and T. von Eicken. Logp: towards a realistic model of parallel computation. In PPOPP '93, pages 1-12, 1993.
5. N. Dalvi, A. Parameswaran, and V. Rastogi. Minimizing uncertainty in pipelines. In NIPS, 2012.
6. S. B. Davidson, S. Khanna, T. Milo, and S. Roy. Using the crowd for top-k and group-by queries. ICDT '13, pages 225-236, 2013.
7. O. Dekel and O. Shamir. Vox populi: Collecting high-quality labels from a crowd. In COLT, 2009.
8. D. Deutch, O. Greenshpan, B. Kostenko, and T. Milo. Using markov chain monte carlo to play trivia. In ICDE, pages 1308-1311, 2011.
9. A. Doan, R. Ramakrishnan, and A. Halevy. Crowdsourcing systems on the world-wide web. Communications of the ACM, 2011.
10. U. Feige, P. Raghavan, D. Peleg, and E. Upfal. Computing with noisy information. SIAM J. Comput., 23:1001-1018, October 1994.
11. S. Guo, A. Parameswaran, and H. Garcia-Molina. So Who Won? Dynamic Max Discovery with the Crowd. In SIGMOD, 2012.
12. J. Horton and L. Chilton. The labor economics of paid crowdsourcing. CoRR, abs/1001.0627, 2010.
13. J. Whitehill et al. Whose vote should count more: Optimal integration of labels from labelers of unknown expertise. In NIPS. 2009.
14. D. R. Karger, S. Oh, and D. Shah. Budget-optimal task allocation for reliable crowdsourcing systems. CoRR, abs/1110.3564, 2011.
15. H. Karloff, S. Suri, and S. Vassilvitskii. A model of computation for mapreduce. In SODA, 2010.
16. R. M. Karp and V. Ramachandran. A survey of parallel algorithms for shared-memory machines. Technical Report UCB/CSD-88-408, EECS Department, University of California, Berkeley, Mar 1988.
17. S. Kochhar, S. Mazzocchi, and P. Paritosh. The anatomy of a large-scale human computation engine. In HCOMP, New York, NY, USA, 2010.
18. P. Koutris and D. Suciu. Parallel evaluation of conjunctive queries. PODS '11, pages 223-234, New York, NY, USA, 2011. ACM.
19. G. Little, L. B. Chilton, M. Goldman, and R. C. Miller. Turkit: tools for iterative tasks on mechanical turk. In HCOMP, 2009.
20. X. Liu, M. Lu, B. C. Ooi, Y. Shen, S. Wu, and M. Zhang. Cdas: a crowdsourcing data analytics system. Proc. VLDB Endow., 5(10):1040-1051, June 2012.
21. M. Bernstein et al. Soylent: a word processor with a crowd inside. UIST '10, pages 313-322, 2010.
22. A. Marcus, E. Wu, D. Karger, S. Madden, and R. Miller. Human-powered sorts and joins. In VLDB, 2012.
23. W. Mason and D. J. Watts. Financial incentives and the "performance of crowds". In HCOMP, 2009.
24. P. Donmez et al. Efficiently learning the accuracy of labeling sources for selective sampling. In KDD, 2009.
25. P. Welinder, P. Perona. Online crowdsourcing: rating annotators and obtaining cost-effective labels. In CVPR, 2010.
26. A. Parameswaran, H. Garcia-Molina, H. Park, N. Polyzotis, A. Ramesh, and J. Widom. Crowdscreen: Algorithms for filtering data with humans. In SIGMOD, 2012.
27. A. Parameswaran, A. D. Sarma, H. Garcia-Molina, N. Polyzotis, and J. Widom. Human-assisted graph search: it's okay to ask questions. In VLDB, 2011.
28. A. Quinn and B. Bederson. Human computation: a survey and taxonomy of a growing field. In CHI, 2011.
29. R. Gomes et al. Crowdclustering. In NIPS, 2011.
30. A. D. Sarma, A. Parameswaran, H. Garcia-Molina, and A. Halevy. Finding with the crowd. In Infolab Technical Report, 2013.
31. B. Settles. Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison, 2009.
32. M. Toomim, T. Kriplean, C. Pörtner, and J. Landay. Utility of humancomputer interactions: toward a science of preference measurement. CHI '11, pages 2275-2284, New York, NY, USA, 2011. ACM.
33. L. G. Valiant. A bridging model for parallel computation. Commun. ACM, 33:103-111, August 1990.
34. L. von Ahn and L. Dabbish. Designing games with a purpose. Commun. ACM, 51(8), 2008.
35. J. Wang, T. Kraska, M. J. Franklin, and J. Feng. Crowder: Crowdsourc-ing entity resolution. PVLDB, 5(11):1483-1494, 2012.
36. L. Wasserman. All of Statistics. Springer, 2003.
37. T. Yan, V. Kumar, and D. Ganesan. Crowdsearch: exploiting crowds for accurate real-time image search on mobile phones. In MobiSys, 2010.