A. Ahn, The party problem (Accessed: 12 december 2015) http://mathforum.org/ mathimages/index

G. E. Andrews, Drowning in the data deluge. Notices Amer, Math. Soc, vol.59, issue.7, p.933941, 2012.

]. A. Calude, Does big data equal big problemsdoes-big-data-equal-big-problems, 2015.

C. Calude, Information and RandomnessAn Algorithmic Perspective, 2002.

C. S. Calude and G. Longo, Classical, quantum and biological randomness as relative, Natural Computing, pp.11047-11062, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01380600

F. Cecconi, M. Cencini, M. Falcioni, and A. Vulpiani, 26 The big data can be used for scientic testing of hypotheses as well as for testing scientic theories and results Predicting the future from the past: An old problem from a modern perspective, American Journal of Physics, issue.11, pp.801001-1008, 2012.

S. Chibbaro, L. Rondoni, and A. Vulpiani, Reductionism, Emergence and Levels of Reality, 2014.
DOI : 10.1007/978-3-319-06361-4
URL : https://hal.archives-ouvertes.fr/hal-01443949

S. B. Cooper, Computability Theory, 2004.

R. Downey and D. Hirschfeldt, Algorithmic Randomness and Complexity, 2010.
DOI : 10.1007/978-0-387-68441-3

G. Ellis and J. Silk, Scientific method: Defend the integrity of physics, Nature, vol.516, issue.7531, pp.321-323, 2014.
DOI : 10.1038/516321a

R. Ferber, Are Correlations any Guide to Predictive Value?, Applied Statistics, vol.5, issue.2, p.113121, 1956.
DOI : 10.2307/2985494

L. Floridi, Big Data and Their Epistemological Challenge, Philosophy & Technology, vol.25, issue.4, p.435437, 2012.
DOI : 10.1007/s13347-012-0093-4

M. Frické, Big data and its epistemology, Journal of the Association for Information Science and Technology, vol.485, issue.5, p.651661, 2015.
DOI : 10.1002/asi.23212

N. Gisin, Quantum Chance: Nonlocality, Teleportation and Other Quantum Marvels
DOI : 10.1007/978-3-319-05473-5

T. Gowers, A new proof of Szemerédi's theorem. Geometric and Functional Analysis, p.465588, 2001.

R. Graham, Some of my favorite problems in Ramsey Theory. INTEGERS, The Electronic Journal of Combinatorial Number Theory, p.2, 2007.

R. Graham and J. H. Spencer, Ramsey Theory, Scientific American, vol.263, issue.1, p.112117, 1990.
DOI : 10.1038/scientificamerican0790-112

A. Grjebine, La dette publique et comment s'en débarrasser

L. Grossman, What's this all about? The massive volume of data that humanity generates is a new kind of problem. The solution is very old: art, Time Magazine, 2015.

C. Homan, Benchmarked: What's the best le compression format? http://www. howtogeek.com/200698/benchmarked-whats-the-best-file-compression-format, 2015.

M. Kac, On the notion of recurrence in discrete stochastic processes, Bulletin of the American Mathematical Society, vol.53, issue.10, p.10021010, 1947.
DOI : 10.1090/S0002-9904-1947-08927-8

B. Khoussainov, Algorithmically Random Universal Algebras
DOI : 10.1142/9789813109032_0005

. Calude, Information and Complexity, World Scientic Series in Information Studies, p.2016

R. Kitchin, Big Data, new epistemologies and paradigm shifts, Big Data & Society, vol.36, issue.1, p.2014
DOI : 10.1177/2053951714528481

G. Longo, On the relevance of negative results. Inuxus, 2008.

G. Longo and M. Montévil, Perspectives on Organisms: Biological Time, Symmetries and Singularities
DOI : 10.1007/978-3-642-35938-5
URL : https://hal.archives-ouvertes.fr/hal-01349797

P. Lynch, The origins of computer weather prediction and climate modeling, Journal of Computational Physics, vol.227, issue.7, p.34313444, 2008.
DOI : 10.1016/j.jcp.2007.02.034

Y. I. Manin, Cognition and Complexity, Information and Complexity. World Scientic Series in Information Studies, p.2016
DOI : 10.1142/9789813109032_0014

C. Montelle, Chasing Shadows: Mathematics, Astronomy, and the Early History of Eclipse Reckoning, 2011.

P. Norvig, All we want are the facts, ma'am, 2008.

C. O. Grady, Louder vowels won't get you laid, and other tales of spurious correlation . http://arstechnica.co.uk/science/2015/06/louder-vowels-wont-get-you- laid-and-other-tales-of-spurious-correlation, 2015.

O. Dictionaries, Spurious (Accessed: 30 november 2015) http://www. oxforddictionaries.com/definition/learner/spurious, 2015.

J. Paris and L. Harrington, A Mathematical Incompleteness in Peano Arithmetic, Handbook of Mathematical Logic, p.11331142, 1977.
DOI : 10.1016/S0049-237X(08)71130-3

A. Rajaraman and J. D. Ullman, Mining of Massive Datasets, 2011.
DOI : 10.1017/CBO9781139058452

D. A. Reed and J. Dongarra, Exascale computing and big data, Communications of the ACM, vol.58, issue.7, pp.56-68, 2015.
DOI : 10.1145/2699414

C. Reinhart and K. Rogo, Growth in a time of debt, American Economic Review, vol.2, p.573578, 2010.

S. Roberts and J. Winters, Linguistic Diversity and Traffic Accidents: Lessons from Statistical Studies of Cultural Traits, PLoS ONE, vol.47, issue.8, pp.70902-2013
DOI : 10.1371/journal.pone.0070902.s001

E. Schmidt, Every 2 days we create as much information as we did up to, 2003.

R. Schutt and C. O. Neil, Doing Data Science. O'Reilly Media, 2014. [48] J. Sessions. The case for growth: Sessions lists benets of discretionary cuts

A. Shen, Around Kolmogorov Complexity: Basic Notions and Results, 2015.
DOI : 10.1007/978-3-319-21852-6_7
URL : https://hal.archives-ouvertes.fr/lirmm-01233758

G. Smith, Standard Deviations: Flawed Assumptions, Tortured Data, and Other Ways to Lie With Statistics, 2014.

J. Smith, From Reinhart & Rogo 's own data: UK GDP increased fastest when debt-to- GDP ratio was highest and the debt ratio came down! http, 2013.

J. M. Stanton, Introduction to Data Science

M. A. Thomas-herndon and R. Pollin, Does high public debt consistently stie economic growth? A critique of Reinhart and Rogo, Cambridge Journal of Economics, vol.38, p.257279, 2014.

N. K. Vereshchagin, Kolmogorov complexity of enumerating finite sets, Information Processing Letters, vol.103, issue.1, pp.34-39, 2007.
DOI : 10.1016/j.ipl.2007.01.006

T. Vigen, Spurious Correlations. Hachette Books, 2015.

C. Walkden, Magic post-graduate lectures: Magic010 ergodic theory lecture 5