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Parallel Techniques for Big Data

Vendredi 22 Mars 2013 de 14h à 15h30, salle 127 à l'IBC, 95 rue de la Galéra 34095 Montpellier cedex 5

Présentation: Patrick Valduriez, Zenith team, INRIA and LIRMM

http://www-sop.inria.fr/members/Patrick.Valduriez/

TitreParallel Techniques for Big DataRésumé: Big data has become a buzzword, referring to massive amounts of data that are very hard to deal with traditional data management tools. In particular, the ability to produce high-value information and knowledge from big data makes it critical for many applications such as decision support, forecasting, business intelligence, research, and (data-intensive) science. Processing and analyzing massive, possibly complex data is a major challenge since solutions must combine new data management techniques (to deal with new kinds of data) with large-scale parallelism in cluster, grid or cloud environments. Parallel data processing has long been exploited in the context of distributed and parallel database systems for highly structured data. But big data encompasses different data formats (documents, sequences, graphs, arrays, …) that require significant extensions to traditional parallel techniques. In this talk, I will discuss such extensions, from the basic techniques and architectures to NoSQL systems and MapReduce.

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Dernière mise à jour le 24/06/2013