The Ophidia project aims to provide a big data analytics platform solution that addresses scientific use cases related to large volumes of multidimensional data. In this work, the Ophidia software infrastructure is discussed in detail, presenting the entire software stack from level-0 (the Ophidia data store) to level-3 (the Ophidia web service front end). In particular, this paper presents the big data cube primitives provided by the Ophidia framework, discussing in detail the most relevant and available data cube manipulation operators. These primitives represent the proper foundations to build more complex data cube operators like the apex one presented in this paper. A massive data reduction experiment on a 1TB climate dataset is also presented to demonstrate the apex workflow in the context of the proposed framework.

Ophidia: A full software stack for scientific data analytics / Fiore, S.; D'Anca, A.; Elia, D.; Palazzo, C.; Williams, D.; Foster, I.; Aloisio, G.. - (2014), pp. 343-350. (Intervento presentato al convegno 2014 International Conference on High Performance Computing and Simulation, HPCS 2014 tenutosi a ita nel 2014) [10.1109/HPCSim.2014.6903706].

Ophidia: A full software stack for scientific data analytics

Fiore S.;
2014-01-01

Abstract

The Ophidia project aims to provide a big data analytics platform solution that addresses scientific use cases related to large volumes of multidimensional data. In this work, the Ophidia software infrastructure is discussed in detail, presenting the entire software stack from level-0 (the Ophidia data store) to level-3 (the Ophidia web service front end). In particular, this paper presents the big data cube primitives provided by the Ophidia framework, discussing in detail the most relevant and available data cube manipulation operators. These primitives represent the proper foundations to build more complex data cube operators like the apex one presented in this paper. A massive data reduction experiment on a 1TB climate dataset is also presented to demonstrate the apex workflow in the context of the proposed framework.
2014
Proceedings of the 2014 International Conference on High Performance Computing and Simulation, HPCS 2014
Piscataway (New Jersey)‎
Institute of Electrical and Electronics Engineers Inc.
978-1-4799-5313-4
978-1-4799-5312-7
978-1-4799-5311-0
Fiore, S.; D'Anca, A.; Elia, D.; Palazzo, C.; Williams, D.; Foster, I.; Aloisio, G.
Ophidia: A full software stack for scientific data analytics / Fiore, S.; D'Anca, A.; Elia, D.; Palazzo, C.; Williams, D.; Foster, I.; Aloisio, G.. - (2014), pp. 343-350. (Intervento presentato al convegno 2014 International Conference on High Performance Computing and Simulation, HPCS 2014 tenutosi a ita nel 2014) [10.1109/HPCSim.2014.6903706].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/331714
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