On the Use of In-memory Analytics Workflows to Compute eScience Indicators from Large Climate Datasets

Alessandro DAnca; Cosimo Palazzo; Donatello Elia; Sandro Fiore; Ioannis Bistinas; Kristin Bottcher; Victoria Bennett; Giovanni Aloisio

doi:10.1109/CCGRID.2017.132

On the Use of In-memory Analytics Workflows to Compute eScience Indicators from Large Climate Datasets

Alessandro DAnca
, Cosimo Palazzo
, Donatello Elia
, Sandro Fiore
, Ioannis Bistinas
, Kristin Bottcher
, Victoria Bennett
, Giovanni Aloisio

Earth and Climate

Research output: Chapter in Book / Report / Conference proceeding › Chapter › Academic › peer-review

Abstract

The need to apply complex algorithms on large volumes of data is boosting the development of technological solutions able to satisfy big data analytics needs in Cloud and HPC environments. In this context Ophidia represents a big data analytics framework for eScience offering a cross-domain solution for managing scientific, multi-dimensional data. It also exploits an in-memory-based distributed data storage and provides support for the submission of complex workflows by means of various interfaces compliant to well-known standards. This paper presents some applications of Ophidia for the computation of climate indicators defined in the CLIPC project, the WPS interface used for the submission and the workflow based approach employed.

Original language	English
Title of host publication	Proceedings - 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017
Publisher	Institute of Electrical and Electronics Engineers, Inc.
Pages	1035-1043
Number of pages	9
ISBN (Electronic)	9781509066100
ISBN (Print)	9781509066100
DOIs	https://doi.org/10.1109/CCGRID.2017.132
Publication status	Published - 10 Jul 2017

Publication series

Name	Proceedings - 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017

Funding

Funders	Funder number
Horizon 2020 Framework Programme	690116

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 13 Climate Action

Keywords

Big data analytics
Climate impact indicator
Performance evaluation
Scientific workflows

Access to Document

10.1109/CCGRID.2017.132

http://www.mendeley.com/research/inmemory-analytics-workflows-compute-escience-indicators-large-climate-datasets

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DAnca, A., Palazzo, C., Elia, D., Fiore, S., Bistinas, I., Bottcher, K., Bennett, V., & Aloisio, G. (2017). On the Use of In-memory Analytics Workflows to Compute eScience Indicators from Large Climate Datasets. In Proceedings - 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017 (pp. 1035-1043). Article 7973812 (Proceedings - 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017). Institute of Electrical and Electronics Engineers, Inc.. https://doi.org/10.1109/CCGRID.2017.132

DAnca, Alessandro ; Palazzo, Cosimo ; Elia, Donatello et al. / On the Use of In-memory Analytics Workflows to Compute eScience Indicators from Large Climate Datasets. Proceedings - 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017. Institute of Electrical and Electronics Engineers, Inc., 2017. pp. 1035-1043 (Proceedings - 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017).

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DAnca, A, Palazzo, C, Elia, D, Fiore, S, Bistinas, I, Bottcher, K, Bennett, V & Aloisio, G 2017, On the Use of In-memory Analytics Workflows to Compute eScience Indicators from Large Climate Datasets. in Proceedings - 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017., 7973812, Proceedings - 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017, Institute of Electrical and Electronics Engineers, Inc., pp. 1035-1043. https://doi.org/10.1109/CCGRID.2017.132

On the Use of In-memory Analytics Workflows to Compute eScience Indicators from Large Climate Datasets. / DAnca, Alessandro; Palazzo, Cosimo; Elia, Donatello et al.
Proceedings - 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017. Institute of Electrical and Electronics Engineers, Inc., 2017. p. 1035-1043 7973812 (Proceedings - 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017).

Research output: Chapter in Book / Report / Conference proceeding › Chapter › Academic › peer-review

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AU - Palazzo, Cosimo

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AU - Fiore, Sandro

AU - Bistinas, Ioannis

AU - Bottcher, Kristin

AU - Bennett, Victoria

AU - Aloisio, Giovanni

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KW - Climate impact indicator

KW - Performance evaluation

KW - Scientific workflows

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DAnca A, Palazzo C, Elia D, Fiore S, Bistinas I, Bottcher K et al. On the Use of In-memory Analytics Workflows to Compute eScience Indicators from Large Climate Datasets. In Proceedings - 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017. Institute of Electrical and Electronics Engineers, Inc. 2017. p. 1035-1043. 7973812. (Proceedings - 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017). doi: 10.1109/CCGRID.2017.132

On the Use of In-memory Analytics Workflows to Compute eScience Indicators from Large Climate Datasets

Abstract

Publication series

Funding

UN SDGs

Keywords

Access to Document

Persistent URL (handle)

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