Ophidia: Toward Big Data Analytics for eScience | Zendy

Sandro Fiore | Zendy; Alessandro D’Anca | Zendy; Cosimo Palazzo | Zendy; Ian Foster | Zendy; D. N. Williams | Zendy; Giovanni Aloisio | Zendy

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Ophidia: Toward Big Data Analytics for eScience

Author(s) -

Sandro Fiore,

Alessandro D’Anca,

Cosimo Palazzo,

Ian Foster,

D. N. Williams,

Giovanni Aloisio

Publication year - 2013

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2013.05.409

Subject(s) - computer science , big data , netcdf , analytics , exploit , implementation , data science , context (archaeology) , software , data analysis , coupled model intercomparison project , interface (matter) , database , climate model , data mining , software engineering , programming language , paleontology , ecology , computer security , bubble , climate change , maximum bubble pressure method , biology , parallel computing

This work introduces Ophidia, a big data analytics research effort aiming at supporting the access, analysis and mining of scientific (n-dimensional array based) data. The Ophidia platform extends, in terms of both primitives and data types, current relational database system implementations (in particular MySQL) to enable efficient data analysis tasks on scientific array-based data. To enable big data analytics it exploits well-known scientific numerical libraries, a distributed and hierarchical storage model and a parallel software framework based on the Message Passing Interface to run from single tasks to more complex dataflows. The current version of the Ophidia platform is being tested on NetCDF data produced by CMCC climate scientists in the context of the international Coupled Model Intercomparison Project Phase 5 (CMIP5)

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