Data Mining and Analytics for eScience Server

Ophidia is the framework that supports data-intensive analysis to exploit parallel computing techniques and smart data distribution methods. It exploits an array-based storage model and a hierarchical storage organisation to divide and distribute multidimensional scientific datasets over multiple nodes.





“I gained practical experience with the Ophidia framework when working on the INAF-CMCC use case on integration into Ophidia of the Flexible Image Transport System (FITS) format, which is the standard data format used in astronomy. This use case aims at extending Ophidia's data-intensive analysis to the astronomical field where also multidimensional scientific datasets are commonly used.  We experienced the powerful functionalities of the INDIGO platform, which provides native support in terms of array data types, in the development of a workflow for the reduction of the astronomical images.

I also tested Ophidia as end-user by downloading the virtual machine that implements all its features. I found the terminal user friendly and the documentation well explained."

Elisa Londero,

Researcher at the Astronomical Observatory of Trieste, Italy


Thanks to the INDIGO-DataCloud solutions a concrete implementation of a distributed multi-model experiment in the context of the Earth System Grid Federation (ESGF) has been implemented. It efficiently joins server-side and parallel processing through Ophidia, end-to-end workflow management with Kepler and cloud computing through the INDIGO PaaS layer.  From an end user perspective, the provided solution drastically reduces the time to solution for this class of experiments and aims at providing a core infrastructural piece not available yet in the current climate scientists’ eco-system.

Sandro Fiore
Director of the Advanced Scientific Computing Division, Euro-Mediterranean Center on Climate Change Foundation (CMCC)

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High-level APIs and Portals