Voir la source de Data Science or not Data Science?

'''Welcome to LIPN Wiki on Big Data and Cloud Computing'''

With more and more data produced every day, we need to pay a special attention on the technologies to use in order to be able to analyze large amount of data. Big Data is often characterized by the 4 V for Volume, Variety, Velocity, Veracity that constitute challenges for the required tools.

[//en.wikipedia.org/wiki/Machine_learning Machine learning] is to extract knowledge from data. In short it's a family of algorithms that transform data into model or description with the aim to predict or categorize data. In this field we use also analytics tools consisting  to present informations in a more readable way as for the  [http://square-predict.net/ Square Predict] project. Others projects related to big data and cloud computing are the [http://www.wendelin.io Wendelin] and the [http://www.resilience-project.org/ Resilience] projects.

The wiki is related to our experience on the [http://www.grid5000.fr Grid5000], [http://www.teralab-datascience.fr Teralab] and [http://cirrus.uspc.fr CIRRUS] testbeds for the study of the Software, Platform, Infrastructure and Network layers that push forward the Data Science field according to an experimental scientific method. We must not confuse the 'scientific problem' term and the 'System scientific problem' term to serve the scientific problem.

'''General discussion on Systems for Big-Data'''
   [[Infrastructure, programming models, frameworks]]
   [https://www.grid5000.fr/mediawiki/index.php/Moving_Data_around_Grid%275000 Tutorial on moving data around Grid'5000]
   [https://www.oreilly.com/ideas/clustering-geolocated-data-using-spark-and-dbscan?imm_mid=0dfe1f&cmp=em-data-na-na-newsltr_20160203&utm_content=bufferaa451&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer Clustering geolocated data using Spark and DBSCAN] (illustrative example)
   [https://databricks.com/blog/2016/02/08/auto-scaling-scikit-learn-with-spark.html Spark and Scikit-Learn] (illustrative example)

'''Our experience is with the following tools:'''
    [http://spark.apache.org/ Apache Spark]
    [https://flink.apache.org/ Apache Flink]
    [https://www.tensorflow.org/ TensorFlow]
    [http://www.wendelin.io Wendelin] See [http://www.nexedi.com/NXD-Document.Blog.Wendelin.Release.0.4.alpha also this link]
    [http://www.slapos.org SlapOS]
    [http://spark-notebook.io/ Spark-notebook]

'''Testbeds we use in conjunction with our experimental method:'''
    [https://www.grid5000.fr/mediawiki/index.php/Grid5000:Home Grid5000]
    [http://cirrus.uspc.fr CIRRUS] @ Université Sorbonne Paris Cité
    [https://www.teralab-datascience.fr/fr/ Teralab]
    [https://aws.amazon.com/fr/ Amazon]

'''Apache Spark'''
   Some [[Apache Spark]] implementations (since 2011/2012)
   [[How to use Spark on Grid5000]]

'''SlapOS cloud'''
   General information on [http://community.slapos.org/wiki SlapOS]
   [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6650981&tag=1 BOINC as a Service for the SlapOS Cloud: Tools and Methods]
   [https://hal.archives-ouvertes.fr/hal-00958012/file/SlapOS.pdf Déploiement de la plate-forme SlapOS dans l'environnement Grid'5000]
   [http://lipn.univ-paris13.fr/~cerin/SyntheseLIPNresilience.pdf Synthesis] of the LIPN work for the FUI [http://www.resilience-project.org/ Resilience] project

'''TeraLab'''
   General information on [//www.teralab-datascience.fr/fr/ TeraLab]
   [[How to use TeraLab]]
   [http://www.nexedi.com/slapos-IMT-Documents.Teralab.Success.Case TeraLab and SlapOS] and [http://www.vifib.com/vifib-Tarif VFIB fees for managing your infrasructure.]

'''Thesis corner'''
   [http://lipn.univ-paris13.fr/~abidi/THESE/These_leila_Abidi.pdf Leila Abidi]: Revisiter les grilles de PCs avec des technologies du Web et le cloud computing (2015)
   [Walid Saad]: Gestion de données pour le calcul scientifique dans les environnements grilles et cloud (2016)
   [Tugdual Sarazin] Apprentissage massivement distribué dans un environnement "Big data" (2016)
   [Mohammed Ghesmoune] Fouille de flux de données massives. Application aux “BigData” d’assurance (2016)
   [Hippolyte Léger] Apprentissage Relationnel Massif (2018)


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