BIGDATA 2013 - International Workshop on Big Data in Science (BIGDATA)

Increasingly, the industrial innovative breakthroughs and the next scientific discoveries will depend on the capacity to extract knowledge and sense from the enormous amount of emph{Big Data} information. Examples vary from processing data provided by scientific instruments such as the CERN's LHC, the LSST Telescope in Chile, or the OOI large-scale underwater sensors network; grabbing, indexing and nearly instantaneously mining and searching the Web; building and traversing the billion-edge social network graphs; anticipating market and customer trends through multiple channels of information. Collecting information from various sources, recognizing patterns and returning human scale results from this ``data deluge'' is the new challenge the community is facing.
The Big Data challenge consists in managing, storing, analyzing and visualizing these ever growing huge datasets to extract sense and knowledge. As the volume of data grows exponentially, the management and the processing of these data becomes more complex in proportion.
The purpose of the workshop is to provide a forum for discussing recent advances, identifying open issues, introducing developments and tools for systems and infrastructures which address the BigData challenge. In addition, the workshop will emphasize on experience learnt from scientific communities, whose computing applications are reshaping traditional computational science to a new form of data-centric parallel and/or distributed computing. A full list of the topics of interest is given below:
Topics of Interest
Data and compute-intensive applications
Scientific data-sets analysis
MapReduce implementation: issues and improvements
BigData life-cycle management, tools and languages
Security issues : data sets sharing, access, and data privacy
Distributed Computing Infrastructures suitable for BigData
Data-intensive computing on hybrid infrastructures
(Grids/Clouds/Desktop Grids)
Use of CDN and P2P techniques
Smart and decentralized data acquisition
Cloud storage architectures for Big Data
Data-intensive Cloud-based applications
Data management within and across multiple cloud data centers
Data management in HPC Clouds
Programming models for data-intensive Cloud computing
Elasticity for Cloud data management systems
Algorithms improving performances of data intense applications.
Experiences learnt from BigData applications
Data I/O
Organization Committee
Gabriel Antoniu (INRIA) Gabriel.Antoniu-AT-inria.fr
Gilles Fedak (INRIA/LIP) gilles.fedak-AT-inria.fr
Frédéric Suter (CC-IN2P3/CNRS) frederic.suter-AT-cc.in2p3.fr