Big Data

30 Pages·2014·13.08 MB·English

by  Gabriel Antoniu

Big Data :
concepts-clés et enjeux
Gabriel Antoniu
DR Inria, Responsable de l’équipe-projet Inria KerData
Centre de Recherche Inria Rennes – Bretagne Atlantique

Forum ORAP
"BIG DATA et HPC »
10 avril 2014, Paris
Big Data : le déluge des données
Rendre possible la collecte,
la recherche et l’analyse
des données
2
Big
 Data:
 The
 Big
 Picture

En 2010 l’univers numérique étai t
1.2 zettaoctets

10 ans plus tard, ce sera
35 zettaoctets
The economist Feb 25 2010
3
La
 croissance
 est
 exponnentielle…

4
Explosion des données en bioinformatique
107 en 14 ans
X
Credits: Ian Foster
5
Big Data = ?
6
“Big Data refers to data sets whose size is beyond the ability
of typical database software tools to capture, store, manage
and analyze.” (McKinsey Global Institute)

“Big Data is the term for a collection of data sets so large and
complex that it becomes difficult to process using on-hand
database management tools or traditional data processing
applications.” (Wikipedia)
D’où
 viennent
 les
 Big
 Data?

7
La “science des données”,
4e paradigme de la découverte scientifique
2
⎛ . ⎞
⎜ a ⎟ 4πGρ c2
= −Κ
⎜⎜ a ⎟⎟ 3 a2
⎝ ⎠

Today and the
Thousand Last few Last
Future
years ago hundred years few decades
Unify theory,
Description of natural Newton’s laws, Simulation of
experiment and
phenomena Maxwell’s equations… complex phenomena
simulation with large
multidisciplinary Data
Using data exploration

and data mining
(from instruments,
sensors, humans…)
Distributed
Crédits: Dennis Gannon Communities
8
La “science des données”,
4e paradigme de la découverte scientifique
2
⎛ . ⎞
⎜ a ⎟ 4πGρ c2
= −Κ
⎜⎜ a ⎟⎟ 3 a2
⎝ ⎠

Today and the
Thousand Last few Last
Future
years ago hundred years few decades
Unify theory,
Description of natural Newton’s laws, Simulation of
experiment and
phenomena Maxwell’s equations… complex phenomena
simulation with large
multidisciplinary Data
Using data exploration

and data mining
(from instruments,
sensors, humans…)
Distributed
Communities
9
The 4th Paradigm – Gordon Bell, 2009
•  3 piliers de la “data science”
•  Capturer, nettoyer, analyser !
•  Livre sur le domaine et ses problématiques
•  Part 1: Earth and Environment
•  Part 2: Health and Wellbeing
•  Part 3: Scientific Infrastructure
•  A new path for science?
•  Beyond the tsunami: developing the infrastructure to deal with life sciences data
•  Multicore computing and scientific discovery
•  Parallelism and the cloud
•  The impact of workflow tools on data-centric research
•  Semantic escience: encoding meaning in next-generation digitally enhanced science
•  Visualization for data-intensive science
•  A platform for all that we know: creating a knowledge-driven research infrastructure
•  Part 4: Scholarly Communication
http://research.microsoft.com/en-us/collaboration/fourthparadigm/
10