Quand: LUNDI 19 SEPTEMBRE 2016 \u00e0 14h00
\nO\u00f9: en A003 \u00e0 T\u00e9l\u00e9com SudParis 9 rue Charles Fourier 91011 Evry Cedex<\/p>\n
Membres du jury :<\/strong><\/p>\n Mme Ana CAVALLI, Professeure, T\u00e9l\u00e9com SudParis, FRANCE – Directeur de these Rapporteurs :<\/strong><\/p>\n Madame Michelle SIBILLA, Professeure, IRIT – Toulouse – FRANCE<\/p>\n Monsieur Laurent D’ORAZIO, Ma\u00eetre de conf\u00e9rences, HDR – Universit\u00e9 de Clermont-Ferrand – FRANCE<\/p>\n R\u00e9sum\u00e9 :<\/em><\/p>\n Dans le monde d\u2019aujourd\u2019hui de multiples acteurs de la technologie num\u00e9rique produisent des quantit\u00e9s infinies de donn\u00e9es. Capteurs, r\u00e9seaux sociaux ou e-commerce, ils g\u00e9n\u00e8rent tous de l\u2019information qui s\u2019incr\u00e9mente en temps-r\u00e9el selon les 3 V de Gartner : en Volume, en Vitesse et en Variabilit\u00e9. Afin d\u2019exploiter efficacement et durablement ces donn\u00e9es, il est important de respecter la dynamicit\u00e9 de leur \u00e9volution chronologique au moyen de deux approches : le polymorphisme d\u2019une part, au moyen d\u2019un mod\u00e8le dynamique capable de supporter le changement de type \u00e0 chaque instant sans failles de traitement ; d\u2019autre part le support de la volatilit\u00e9 par un mod\u00e8le intelligent prenant en compte des donn\u00e9es cl\u00e9 seulement interpr\u00e9tables \u00e0 un instant \u00ab t \u00bb, au lieu de traiter toute la volum\u00e9trie des donn\u00e9es actuelle et historique. L\u2019objectif premier de cette \u00e9tude est de pouvoir \u00e9tablir au moyen de ces approches une vision int\u00e9gratrice du cycle de vie des donn\u00e9es qui s\u2019\u00e9tablit selon 3 \u00e9tapes, (1) la synth\u00e8se des donn\u00e9es via la s\u00e9lection des valeurs-cl\u00e9s des micro-donn\u00e9es acquises par les diff\u00e9rents op\u00e9rateurs au niveau de la source, (2) la fusion en faisant le tri des valeurs-cl\u00e9s s\u00e9lectionn\u00e9es et les dupliquant suivant un aspect de d\u00e9-normalisation afin d\u2019obtenir un traitement plus rapide des donn\u00e9es et (3) la transformation en un format particulier de carte de cartes de cartes, via Hadoop dans le processus classique de MapReduce afin d\u2019obtenir un graphe d\u00e9fini dans la couche applicative. Cette r\u00e9flexion est en outre soutenue par un prototype logiciel mettant en \u0153uvre les op\u00e9rateurs de mod\u00e9lisation sus-d\u00e9crits et aboutissant \u00e0 une bo\u00eete \u00e0 outils de mod\u00e9lisation comparable \u00e0 un AGL et, permettant une mise en place assist\u00e9e d’un ou plusieurs traitements sur BigData.<\/p>\n Abstract :<\/em><\/p>\n Nowadays, multiple actors of Internet technology are producing very large amounts of data. Sensors, social media or e-commerce, all generate real-time extending information based on the 3 Vs of Gartner: Volume, Velocity and Variety. In order to efficiently exploit this data, it is important to keep track of the dynamic aspect of their chronological evolution by means of two main approaches: the polymorphism, a dynamic model able to support type changes every second with a successful processing and second, the support of data volatility by means of an intelligent model taking in consideration key-data, salient and valuable at a specific moment without processing all volumes of history and up to date data. The primary goal of this study is to establish, based on these approaches, an integrative vision of data life cycle set on 3 steps, (1) data synthesis by selecting key-values of micro-data acquired by different data source operators, (2) data fusion by sorting and duplicating the selected key-values based on a de-normalization aspect in order to get a faster processing of data and (3) the data transformation into a specific format of map of maps of maps, via Hadoop in the standard MapReduce process, in order to define the related graph in applicative layer. In addition, this study is supported by a software prototype using the already described modeling tools, as a toolbox compared to an automatic programming software and allowing to create a customized processing chain of BigData.<\/p>\n","protected":false},"excerpt":{"rendered":" Quand: LUNDI 19 SEPTEMBRE 2016 \u00e0 14h00 O\u00f9: en A003 \u00e0 T\u00e9l\u00e9com SudParis 9 rue Charles Fourier 91011 Evry Cedex Membres du jury : Mme Ana CAVALLI, Professeure, T\u00e9l\u00e9com SudParis, FRANCE – Directeur de these Mme No\u00ebmie SIMONI, Professeure, Telecom ParisTech, FRANCE – Examinateur Mme Karine ZEITOUNI, Professeure, Universit\u00e9 de Versaille St Quentin (UVSQ), FRANCE 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\nMme No\u00ebmie SIMONI, Professeure, Telecom ParisTech, FRANCE – Examinateur
\nMme Karine ZEITOUNI, Professeure, Universit\u00e9 de Versaille St Quentin (UVSQ), FRANCE – Examinateur
\nM. Daniel RANC, Ing\u00e9nieur d’\u00e9tudes, T\u00e9l\u00e9com SudParis, FRANCE – Co-encadrant de these
\nMme Genoveva VARGAS-SOLAR, Professeure, Laboratory of Informatics of Grenoble, FRANCE – Examinateur
\nM. Florent MASSEGLIA, Professeur, Universit\u00e9 Montpellier 2 – Campus St Priest, FRANCE – Examinateur<\/p>\n