The availability of systems able to process and analyse big amount of data has boosted scientific advances in several fields. Workflows provide an effective tool to define and manage large sets of processing tasks. In the big data analytics area, the Ophidia project provides a cross-domain big data analytics framework for the analysis of scientific, multi-dimensional datasets. The framework exploits a server-side, declarative, parallel approach for data analysis and mining. It also features a complete workflow management system to support the execution of complex scientific data analysis, schedule tasks submission, manage operators dependencies and monitor jobs execution. The workflow management engine allows users to perform a coordinated execution of multiple data analytics operators (both single and massive - parameter sweep) in an effective manner. For the definition of the big data analytics workflow, a JSON schema has been properly designed and implemented. To aid the definition of the workflows, a visual design language consisting of several symbols, named Data Analytics Workflow Modelling Language (DAWML), has been also defined.
A workflow-enabled big data analytics software stack for escience / Palazzo, C.; Mariello, A.; Fiore, S.; D'Anca, A.; Elia, D.; Williams, D. N.; Aloisio, G.. - (2015), pp. 545-552. (Intervento presentato al convegno 13th International Conference on High Performance Computing and Simulation, HPCS 2015 tenutosi a Amsterdam, the Netherlands nel 2015) [10.1109/HPCSim.2015.7237088].
A workflow-enabled big data analytics software stack for escience
Mariello A.;Fiore S.;
2015-01-01
Abstract
The availability of systems able to process and analyse big amount of data has boosted scientific advances in several fields. Workflows provide an effective tool to define and manage large sets of processing tasks. In the big data analytics area, the Ophidia project provides a cross-domain big data analytics framework for the analysis of scientific, multi-dimensional datasets. The framework exploits a server-side, declarative, parallel approach for data analysis and mining. It also features a complete workflow management system to support the execution of complex scientific data analysis, schedule tasks submission, manage operators dependencies and monitor jobs execution. The workflow management engine allows users to perform a coordinated execution of multiple data analytics operators (both single and massive - parameter sweep) in an effective manner. For the definition of the big data analytics workflow, a JSON schema has been properly designed and implemented. To aid the definition of the workflows, a visual design language consisting of several symbols, named Data Analytics Workflow Modelling Language (DAWML), has been also defined.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione
