S. Fiore, Marco Mancini, D. Elia, P. Nassisi, F. Brasileiro, I. Blanquer, Iana A. A. Rufino, A. Seijmonsbergen, Carlos de Oliveira Galvao, V. Canhos, Andrea Mariello, Cosimo Palazzo, A. Nuzzo, Alessandro D'Anca, G. Aloisio
{"title":"Big data analytics for climate change and biodiversity in the EUBrazilCC federated cloud infrastructure","authors":"S. Fiore, Marco Mancini, D. Elia, P. Nassisi, F. Brasileiro, I. Blanquer, Iana A. A. Rufino, A. Seijmonsbergen, Carlos de Oliveira Galvao, V. Canhos, Andrea Mariello, Cosimo Palazzo, A. Nuzzo, Alessandro D'Anca, G. Aloisio","doi":"10.1145/2742854.2747282","DOIUrl":null,"url":null,"abstract":"The analysis of large volumes of data is key for knowledge discovery in several scientific domains such as climate, astrophysics, life sciences among others. It requires a large set of computational and storage resources, as well as flexible and efficient software solutions able to dynamically exploit the available infrastructure and address issues related to data volume, distribution, velocity and heterogeneity. This paper presents a data-driven and cloud-based use case implemented in the context of the EUBrazilCC project for the analysis of climate change and biodiversity data. The use case architecture and main components, as well as a Platform as a Service (PaaS) framework for big data analytics named PDAS, together with its elastic deployment in the EUBrazilCC federated cloud infrastructure are presented and discussed in detail.","PeriodicalId":417279,"journal":{"name":"Proceedings of the 12th ACM International Conference on Computing Frontiers","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 12th ACM International Conference on Computing Frontiers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2742854.2747282","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
The analysis of large volumes of data is key for knowledge discovery in several scientific domains such as climate, astrophysics, life sciences among others. It requires a large set of computational and storage resources, as well as flexible and efficient software solutions able to dynamically exploit the available infrastructure and address issues related to data volume, distribution, velocity and heterogeneity. This paper presents a data-driven and cloud-based use case implemented in the context of the EUBrazilCC project for the analysis of climate change and biodiversity data. The use case architecture and main components, as well as a Platform as a Service (PaaS) framework for big data analytics named PDAS, together with its elastic deployment in the EUBrazilCC federated cloud infrastructure are presented and discussed in detail.
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