A. Arasu, S. Babu, and J. Widom, CQL: A Language for Continuous Queries over Streams and Relations, pp.1-19, 2004.
DOI : 10.1007/978-3-540-24607-7_1

Y. Bengio, Deep learning of representations for unsupervised and transfer learning, Proceedings of the 2011 International Conference on Unsupervised and Transfer Learning Workshop, pp.17-37, 2011.

Y. Bengio, Practical recommendations for gradient-based training of deep architectures . CoRR, abs/1206, 2012.

Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, Greedy layer-wise training of deep networks, Proceedings of the 19th International Conference on Neural Information Processing Systems, NIPS'06, pp.153-160, 2006.

L. Breiman, Random forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001.
DOI : 10.1023/A:1010933404324

J. H. Friedman, machine., The Annals of Statistics, vol.29, issue.5, pp.1189-1232, 2001.
DOI : 10.1214/aos/1013203451

P. Geurts, D. Ernst, and L. Wehenkel, Extremely randomized trees, Machine Learning, vol.63, issue.1, pp.3-42, 2006.
DOI : 10.1007/s10994-006-6226-1

URL : https://hal.archives-ouvertes.fr/hal-00341932

A. Hinneburg, C. C. Aggarwal, and D. A. Keim, What is the nearest neighbor in high dimensional spaces?, Proceedings of the 26th International Conference on Very Large Data Bases, VLDB '00, pp.506-515, 2000.

P. Diederik, J. Kingma, and . Ba, Adam: A method for stochastic optimization. CoRR, abs/1412, 2014.

B. Li, Y. Diao, and P. Shenoy, Supporting scalable analytics with latency constraints, Proc. VLDB Endow, pp.1166-1177, 2015.
DOI : 10.14778/2809974.2809979

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine learning in python, J. Mach. Learn. Res, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

S. Rifai, P. Vincent, X. Muller, X. Glorot, and Y. Bengio, Contractive auto-encoders: Explicit invariance during feature extraction, Proceedings of the 28th International Conference on International Conference on Machine Learning, pp.833-840

A. J. Smola and B. Schölkopf, A tutorial on support vector regression, Statistics and Computing, vol.14, issue.3, pp.199-222, 2004.
DOI : 10.1023/B:STCO.0000035301.49549.88

D. Van-aken, A. Pavlo, G. J. Gordon, and B. Zhang, Automatic Database Management System Tuning Through Large-scale Machine Learning, Proceedings of the 2017 ACM International Conference on Management of Data , SIGMOD '17, pp.1009-1024, 2017.
DOI : 10.1145/2588555.2593678

M. Zaharia, T. Das, H. Li, S. Shenker, and I. Stoica, Discretized streams: An efficient and fault-tolerant model for stream processing on large clusters, Proceedings of the 4th USENIX Conference on Hot Topics in Cloud Ccomputing, HotCloud'12, pp.10-10
DOI : 10.21236/ada575859

URL : http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-259.pdf