P. Meier, Digital Humanitarians: How Big Data Is Changing the Face of Humanitarian Response, 2015.
DOI : 10.1201/b18023

K. Benouaret, R. Valliyur-ramalingam, and F. Charoy, Answering complex locationbased queries with crowdsourcing, 9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing, pp.438-447, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00877357

H. Purohit, A. Hampton, S. Bhatt, V. Shalin, A. Sheth et al., Identifying Seekers and Suppliers in Social Media Communities to Support Crisis Coordination, Computer Supported Cooperative Work (CSCW), vol.25, issue.4, 2014.
DOI : 10.1007/s10606-014-9209-y

F. Alt, A. S. Shirazi, A. Schmidt, U. Kramer, and Z. Nawaz, Location-based crowdsourcing, Proceedings of the 6th Nordic Conference on Human-Computer Interaction Extending Boundaries, NordiCHI '10, pp.13-22, 2010.
DOI : 10.1145/1868914.1868921

M. F. Bulut, Y. S. Yilmaz, and M. Demirbas, Crowdsourcing location-based queries, 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp.513-518, 2011.
DOI : 10.1109/PERCOMW.2011.5766944

L. Kazemi and C. Shahabi, GeoCrowd, Proceedings of the 20th International Conference on Advances in Geographic Information Systems, SIGSPATIAL '12, pp.189-198, 2012.
DOI : 10.1145/2424321.2424346

S. Guo, A. Parameswaran, and H. Garcia-molina, So who won? dynamic max discovery with the crowd, 2011.

J. C. Pomerol and S. Barba-romero, Multicriterion Decision in Management: Principles and Practice, 2012.
DOI : 10.1007/978-1-4615-4459-3

R. M. Adelsman and A. B. Whinston, Sophisticated voting with information for two voting functions, Journal of Economic Theory, vol.15, issue.1, pp.145-159, 1977.
DOI : 10.1016/0022-0531(77)90073-4

B. Eriksson, Learning to top-k search using pairwise comparisons, Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2013, pp.265-273, 2013.

T. Pfeiffer, X. A. Gao, and D. G. Rand, Adaptive polling for information aggregation, In: AAAI, 2012.

. Peng-ye and D. Doermann, Combining preference and absolute judgements in a crowd-sourced setting, ) ICML'13 workshop: Machine Learning Meets Crowdsourcing, 2013.

S. B. Davidson, S. Khanna, T. Milo, and S. Roy, Using the crowd for top-k and group-by queries, Proceedings of the 16th International Conference on Database Theory, ICDT '13, pp.225-236, 2013.
DOI : 10.1145/2448496.2448524

U. Feige, P. Raghavan, D. Peleg, and E. Upfal, Computing with Noisy Information, SIAM Journal on Computing, vol.23, issue.5, pp.1001-1018, 1994.
DOI : 10.1137/S0097539791195877

A. R. Khan and H. Garcia-molina, Hybrid strategies for finding the max with the crowd: Technical report, 2014.

F. Wauthier, M. Jordan, and N. Jojic, Efficient ranking from pairwise comparisons, Proceedings of the 30th International Conference on Machine Learning (ICML-13 Conference Proceedings, pp.109-117, 2013.

M. S. Bernstein, G. Little, R. C. Miller, B. Hartmann, M. S. Ackerman et al., Soylent: a word processor with a crowd inside, pp.313-322, 2010.