P. C. Adams, M. Speechley, and A. E. Kertesz, Long-term survival analysis in hereditary hemochromatosis, Gastroenterology, vol.101, issue.2, pp.368-372, 1991.

T. Alexandrov, S. Bianconcini, E. B. Dagum, P. Maass, and T. S. Mcelroy, A Review of Some Modern Approaches to the Problem of Trend Extraction, Econometric Reviews, vol.31, issue.6, pp.593-624, 2012.

K. J. Archer and R. V. Kimes, Empirical characterization of random forest variable importance measures, Computational Statistics & Data Analysis, vol.52, issue.4, pp.2249-2260, 2008.

D. Barber, Bayesian Reasoning and Machine Learning. Cambridge University Press, p.0, 2012.

J. L. Binet, A. Auquier, G. Dighiero, C. Chastang, H. Piguet et al., A new prognostic classification of chronic lymphocytic leukemia derived from a multivariate survival analysis, Cancer, vol.48, issue.1, pp.198-206, 1981.

C. M. Bishop, Pattern Recognition and Machine Learning, 2006.

L. Bottou, Large-Scale Machine Learning with Stochastic Gradient Descent, Proceedings of COMPSTAT'2010, pp.177-186, 2010.

S. P. Boyd and L. Vandenberghe, Convex Optimization, 2004.

L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen, Classification and Regression Trees, 1984.

E. Brill, A Simple Rule-based Part of Speech Tagger, Proceedings of the Workshop on Speech and Natural Language, HLT '91, pp.112-116, 1992.

M. E. Califf and R. J. Mooney, Relational Learning of Pattern-match Rules for Information Extraction, Proceedings of the Sixteenth National Conference on Artificial Intelligence and the Eleventh Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence, AAAI '99/IAAI '99, pp.328-334, 1999.

R. Caruana and A. Niculescu-mizil, An Empirical Comparison of Supervised Learning Algorithms, Proceedings of the 23rd International Conference on Machine Learning, ICML '06, pp.161-168, 2006.

J. Chen, K. Li, Z. Tang, K. Bilal, S. Yu et al., A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment, IEEE Transactions on Parallel and Distributed Systems, vol.28, issue.4, pp.919-933, 2017.

H. Choi, K. Cho, and Y. Bengio, Context-dependent word representation for neural machine translation, Computer Speech & Language, vol.45, pp.149-160, 2017.

W. W. Cohen, P. Ravikumar, and S. E. Fienberg, A Comparison of String Distance Metrics for Name-matching Tasks, Proceedings of the 2003 International Conference on Information Integration on the Web, IIWEB'03, pp.73-78, 2003.

P. Comon, Independent Component Analysis, a New Concept? Signal Process, vol.36, pp.287-314, 1994.

P. Cortez, A. Cerdeira, F. Almeida, T. Matos, and J. Reis, , 2009.

, Modeling wine preferences by data mining from physicochemical properties, Decision Support Systems, vol.47, issue.4, pp.547-553

D. R. Cox and D. Oakes, Analysis of survival data, p.21, 1984.

J. Dahl and L. Vandenberghe, Cvxopt: A python package for convex optimization, Proc. eur. conf. op. res, 2006.

S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman, Indexing by latent semantic analysis, Journal of the American society for information science, vol.41, issue.6, p.391, 1990.

L. Deleger, C. Grouin, and P. Zweigenbaum, Extracting medical information from narrative patient records: the case of medication-related information, Journal of the American Medical Informatics Association : JAMIA, vol.17, issue.5, pp.555-558, 2010.

A. P. Dempster, N. M. Laird, R. , and D. B. , Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, Series B, vol.39, issue.1, pp.1-38, 1977.

K. Duan, S. S. Keerthi, and A. N. Poo, Evaluation of simple performance measures for tuning SVM hyperparameters, Neurocomputing, vol.51, pp.41-59, 2003.

J. B. Elsner and A. A. Tsonis, Singular Spectrum Analysis: A New Tool in Time Series Analysis, p.5, 2013.

R. Fan, K. Chang, C. Hsieh, X. Wang, L. et al., Liblinear: A library for large linear classification, Journal of machine learning research, vol.9, pp.1871-1874, 2008.

P. Flandrin, P. Gonçalvès, R. , and G. , Detrending and denoising with empirical mode decompositions, p.12, 2004.
URL : https://hal.archives-ouvertes.fr/inria-00570614

, European Signal Processing Conference, pp.1581-1584

J. Foulds and E. Frank, A review of multi-instance learning assumptions, The Knowledge Engineering Review, vol.25, issue.01, pp.1-25, 2010.

Z. Fu, A. Robles-kelly, and J. Zhou, Milis: Multiple instance learning with instance selection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.5, pp.958-977, 2011.

S. Golchi, D. Bingham, H. Chipman, and D. Campbell, , 2015.

, Monotone Emulation of Computer Experiments. SIAM/ASA Journal on Uncertainty Quantification, vol.3, issue.1, pp.370-392

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, 2016.

M. Grbovic, N. Djuric, V. Radosavljevic, F. Silvestri, and N. Bhamidipati, Context-and Content-aware Embeddings for Query Rewriting in Sponsored Search, Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '15, pp.383-392, 2015.

A. J. Gross and V. Clark, Survival Distributions: Reliability Applications in the Biomedical Sciences, 1975.

X. Gu, S. Papadimitriou, P. S. Yu, C. , and S. P. , , 2008.

, Online failure forecast for fault-tolerant data stream processing, IEEE 24th International Conference on Data Engineering, pp.1388-1390, 2008.

A. C. Harvey and T. M. Trimbur, General Model-Based Filters for Extracting Cycles and Trends in Economic Time Series, The Review of Economics and Statistics, vol.85, issue.2, pp.244-255, 2003.

J. Huet, S. Besseau, B. Maillard, and F. Michaud, Method and computer program for the maintenance aid of aircraft equipment, p.272, 2015.

J. D. Hunter, Matplotlib: A 2d Graphics Environment, Computing in Science & Engineering, vol.9, issue.3, pp.90-95, 2007.
DOI : 10.1109/mcse.2007.55

I. S. Iokhvidov, Hankel and Toeplitz matrices and forms: algebraic theory, 1982.

A. Ittoo, L. M. Nguyen, . Van-den, and A. Bosch, Text analytics in industry: Challenges, desiderata and trends, Computers in Industry, vol.78, pp.96-107, 2016.

T. Joachims, Text categorization with support vector machines: Learning with many relevant features, European conference on machine learning, pp.137-142, 1998.

K. A. Kaiser and N. Z. Gebraeel, Predictive maintenance management using sensor-based degradation models. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, vol.39, issue.4, pp.840-849, 2009.

E. L. Kaplan and P. Meier, Nonparametric Estimation from Incomplete Observations, Journal of the American Statistical Association, vol.53, issue.282, pp.457-481, 1958.

S. Kauschke, J. Fürnkranz, J. , and F. , Predicting cargo train failures: A machine learning approach for a lightweight prototype, International Conference on Discovery Science, pp.151-166, 2016.

P. J. Kelly, L. L. Lim, and .. , Survival analysis for recurrent event data: an application to childhood infectious diseases, Statistics in Medicine, vol.19, issue.1, pp.13-33, 2000.

J. P. Klein and M. L. Moeschberger, Survival Analysis: Techniques for Censored and Truncated Data, 2005.

Y. S. Koh, Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection: Technologies for Infrequent and Critical Event Detection, vol.3, 2009.

R. Kohavi, A Study of Cross-validation and Bootstrap for Accuracy Estimation and Model Selection, Proceedings of the 14th International Joint Conference on Artificial Intelligence, vol.2, pp.1137-1143, 1995.

D. Koller and N. Friedman, Probabilistic Graphical Models: Principles and Techniques, pp.7-11, 2009.

T. K. Landauer, P. W. Foltz, and D. Laham, An introduction to latent semantic analysis, Discourse Processes, vol.25, pp.259-284, 1998.

S. Laxman, V. Tankasali, and R. W. White, Stream prediction using a generative model based on frequent episodes in event sequences, Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '08, pp.453-461, 2008.

E. T. Lee and J. W. Wang, Statistical Methods for Survival Data Analysis, p.9, 2013.

H. Liao, W. Zhao, G. , and H. , Predicting remaining useful life of an individual unit using proportional hazards model and logistic regression model, RAMS '06. Annual Reliability and Maintainability Symposium, pp.127-132, 2006.

A. Liaw and M. Wiener, Classification and regression by randomforest, vol.2, pp.18-22, 2002.

F. Liu, D. Pennell, F. Liu, and Y. Liu, Unsupervised Approaches for Automatic Keyword Extraction Using Meeting Transcripts, Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL '09, pp.620-628, 2009.
DOI : 10.3115/1620754.1620845
URL : http://dl.acm.org/ft_gateway.cfm?id=1620845&type=pdf

Y. Liu, Z. Liu, T. Chua, and M. Sun, Topical Word Embeddings, 2015.

Z. Ma and A. W. Krings, Survival Analysis Approach to Reliability, Survivability and Prognostics and Health Management (PHM), IEEE Aerospace Conference, pp.1-20, 2008.
DOI : 10.1109/aero.2008.4526634

A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng et al., Learning Word Vectors for Sentiment Analysis, 2011.

, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol.1, pp.142-150

X. Meng, J. Bradley, B. Yavuz, E. Sparks, S. Venkataraman et al., , 2016.

, MLlib: Machine Learning in Apache Spark, J. Mach. Learn. Res, vol.17, issue.1, pp.1235-1241

F. Mhamdi, J. Poggi, J. , and M. , Trend extraction for seasonal time series using ensemble empirical mode decomposition, Advances in Adaptive Data Analysis, issue.03, pp.363-383, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00942961

T. Mikolov, K. Chen, G. Corrado, D. , and J. , Efficient Estimation of Word Representations in Vector Space, 2013.

T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, D. et al., Distributed Representations of Words and Phrases and their Compositionality, Advances in Neural Information Processing Systems, vol.26, pp.3111-3119, 2013.

T. Mikolov, W. Yih, and G. Zweig, Linguistic regularities in continuous space word representations, HLT-NAACL, pp.746-751, 2013.

R. G. Miller, Survival Analysis, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00561319

J. F. Murray, G. F. Hughes, and K. Kreutz-delgado, Machine learning methods for predicting failures in hard drives: A multiple-instance application, J. Mach. Learn. Res, vol.6, pp.783-816, 2005.

T. Nakagawa and S. Osaki, The discrete weibull distribution, IEEE Transactions on Reliability, vol.24, issue.5, pp.300-301, 1975.
DOI : 10.1109/tr.1975.5214915

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

J. Riihimaki and A. Vehtari, Gaussian processes with monotonicity information, PMLR, pp.645-652, 2010.

S. Ruder, An overview of gradient descent optimization algorithms, 2016.

L. Saeeda, Iterative Approach for Information Extraction and Ontology Learning from Textual Aviation Safety Reports, 2017.
DOI : 10.1007/978-3-319-58451-5_18

, The Semantic Web, pp.236-245

F. Salfner, M. Lenk, and M. Malek, A survey of online failure prediction methods, ACM Computing Surveys (CSUR), vol.42, issue.3, p.10, 2010.
DOI : 10.1145/1670679.1670680

G. K. Savova, J. J. Masanz, P. V. Ogren, J. Zheng, S. Sohn et al., Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications, Journal of the American Medical Informatics Association, vol.17, issue.5, pp.507-513, 2010.
DOI : 10.1136/jamia.2009.001560
URL : https://academic.oup.com/jamia/article-pdf/17/5/507/5940551/17-5-507.pdf

O. Schwarzkopf, The Extensible Drawing Editor Ipe, Proceedings of the Eleventh Annual Symposium on Computational Geometry, SCG '95, pp.410-411, 1995.

F. Sha, Y. Lin, L. K. Saul, and D. D. Lee, Multiplicative Updates for Nonnegative Quadratic Programming, Neural Computation, vol.19, issue.8, pp.2004-2031, 2007.

F. Sha, L. K. Saul, and D. D. Lee, Multiplicative Updates for Nonnegative Quadratic Programming in Support Vector, 2003.
DOI : 10.1162/neco.2007.19.8.2004
URL : https://repository.upenn.edu/cgi/viewcontent.cgi?article=1639&context=ese_papers

. I. Machines, S. Becker, S. Thrun, and K. Obermayer, Advances in Neural Information Processing Systems 15, pp.1065-1072

B. Siklosi, A. Novák, P. , and G. , Context-Aware Correction of Spelling Errors in Hungarian Medical Documents, 2013.

, Statistical Language and Speech Processing, pp.248-259

R. Sipos, D. Fradkin, F. Moerchen, W. , and Z. , Logbased predictive maintenance, Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, pp.1867-1876, 2014.

J. Son, Q. Zhou, S. Zhou, X. Mao, S. et al., Evaluation and comparison of mixed effects model based prognosis for hard failure, IEEE Transactions on Reliability, vol.62, issue.2, pp.379-394, 2013.

A. Sordoni, Y. Bengio, H. Vahabi, C. Lioma, J. Grue-simonsen et al., A Hierarchical Recurrent EncoderDecoder for Generative Context-Aware Query Suggestion, Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM '15, pp.553-562, 2015.
DOI : 10.1145/2806416.2806493
URL : http://arxiv.org/pdf/1507.02221

G. A. Susto and A. Beghi, Dealing with time-series data in predictive maintenance problems, Emerging Technologies and Factory Automation (ETFA), pp.1-4, 2016.

L. Tanguy, N. Tulechki, A. Urieli, E. Hermann, R. et al., Natural language processing for aviation safety reports: From classification to interactive analysis, Computers in Industry, vol.78, pp.80-95, 2016.
DOI : 10.1016/j.compind.2015.09.005
URL : https://hal.archives-ouvertes.fr/halshs-01322238

S. Theodoridis, Machine Learning: A Bayesian and Optimization Perspective, 2015.

R. Tibshirani, Regression Shrinkage and Selection via the Lasso, Journal of the Royal Statistical Society. Series B (Methodological), vol.58, issue.1, pp.267-288, 1996.
DOI : 10.1111/j.2517-6161.1996.tb02080.x

A. J. Tixier, M. R. Hallowell, B. Rajagopalan, and D. Bowman, Automated content analysis for construction safety: A natural language processing system to extract precursors and outcomes from unstructured injury reports, vol.62, pp.45-56, 2016.

A. J. Tixier, M. Vazirgiannis, and M. R. Hallowell, Word Embeddings for the Construction Domain, 2016.

K. Toutanova, D. Klein, C. D. Manning, and Y. Singer, , 2003.

, Feature-rich Part-of-speech Tagging with a Cyclic Dependency Network, Proceedings of the 2003 Conference of the North American Chapter, vol.1, pp.173-180

L. Ulanova, T. Yan, H. Chen, G. Jiang, E. Keogh et al., Efficient Long-Term Degradation Profiling in Time Series for Complex Physical Systems, Proceedings of the 21th, 2015.

, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '15, pp.2167-2176

L. Ulanova, T. Yan, H. Chen, G. Jiang, E. Keogh et al., Efficient long-term degradation profiling in time series for complex physical systems, Proceedings of the 21th, 2015.

, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '15, pp.2167-2176

P. Wang, Y. Li, and C. K. Reddy, Machine Learning for Survival Analysis: A Survey, 2017.

X. Wang and J. Berger, Estimating Shape Constrained Functions Using Gaussian Processes, SIAM/ASA Journal on Uncertainty Quantification, vol.4, issue.1, pp.1-25, 2016.

Y. Watanabe, H. Otsuka, M. Sonoda, S. Kikuchi, and Y. Matsumoto, Online failure prediction in cloud datacenters by real-time message pattern learning, Cloud Computing Technology and Science (CloudCom), pp.504-511, 2012.

S. Weisberg, Applied Linear Regression, p.0, 2005.

G. M. Weiss and H. Hirsh, Learning to predict rare events in event sequences, KDD, pp.359-363, 1998.

Z. Wu, N. E. Huang, S. R. Long, and C. Peng, On the trend, detrending, and variability of nonlinear and nonstationary time series, Proceedings of the National Academy of Sciences, vol.104, issue.38, pp.14889-14894, 2007.

L. Yu, Z. Zheng, Z. Lan, and S. Coghlan, Practical online failure prediction for blue gene/p: Period-based vs event-driven, 2011 IEEE/IFIP 41st International Conference on, pp.259-264, 2011.

Y. Yuan, S. Zhou, C. Sievenpiper, K. Mannar, and Y. Zheng, Event log modeling and analysis for system failure prediction, IIE Transactions, vol.43, issue.9, pp.647-660, 2011.

M. Zaharia, R. S. Xin, P. Wendell, T. Das, M. Armbrust et al., Apache Spark: A Unified Engine for Big Data Processing, Commun. ACM, vol.59, issue.11, pp.56-65, 2016.

C. Zhang and Y. Ma, Ensemble Machine Learning: Methods and Applications, 2012.

K. Zhang, J. Xu, M. R. Min, G. Jiang, K. Pelechrinis et al., Automated it system failure prediction: A deep learning approach, 2016 IEEE International Conference on, pp.1291-1300, 2016.

Z. Zheng, Z. Lan, B. H. Park, G. , and A. , System log pre-processing to improve failure prediction, Dependable Systems & Networks, 2009. DSN'09. IEEE/IFIP International Conference on, pp.572-577, 2009.

Q. Zhou, J. Son, S. Zhou, X. Mao, S. et al., , 2014.

, Remaining useful life prediction of individual units subject to hard failure, IIE Transactions, vol.46, issue.10, pp.1017-1030

Z. Zhou, Ensemble Methods: Foundations and Algorithms, 2012.