Big Data Analytics on Large-Scale Scientific Datasets in the INDIGO-DataCloud Project

被引：7

作者：

Fiore, Sandro ^{[1
]}

Palazzo, Cosimo ^{[1
]}

D'Anca, Alessandro ^{[1
]}

Elia, Donatello ^{[1
]}

Londero, Elisa ^{[2
]}

Knapic, Cristina ^{[2
]}

Monna, Stephen ^{[3
]}

Marcucci, Nicola M. ^{[3
]}

Aguilar, Fernando ^{[4
]}

Plociennik, Marcin ^{[5
]}

De Lucas, Jesus E. Marco ^{[4
]}

Aloisio, Giovanni ^{[6
]}

机构：

[1] Euromediterranean Ctr Climate Change Fdn, Lecce, Italy

[2] INAF Trieste Astron Observ OATs, Trieste, Italy

[3] Ist Nazl Geofis & Vulcanol INGV, Rome, Italy

[4] UC CSIC, Inst Fis Cantabria, Santander, Spain

[5] IBCh Pas, PSNC Poznan Supercomp & Networking Ctr, Poznan, Poland

[6] Univ Salento, Euromediterranean Ctr Climate Change Fdn, Lecce, Italy

来源：

ACM INTERNATIONAL CONFERENCE ON COMPUTING FRONTIERS 2017 | 2017年

关键词：

Workflow; big data; scientific use case; ensemble analysis;

D O I：

10.1145/3075564.3078884

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In the context of the EU H2020 INDIGO-DataCloud project several use case on large scale scientific data analysis regarding different research communities have been implemented. All of them require the availability of large amount of data related to either output of simulations or observed data from sensors and need scientific (big) data solutions to run data analysis experiments. More specifically, the paper presents the case studies related to the following research communities: (i) the European Multidisciplinary Seafloor and water column Observatory (INGV-EMSO), (ii) the Large Binocular Telescope, (iii) LifeWatch, and (iv) the European Network for Earth System Modelling (ENES).

引用

页码：343 / 348

页数：6

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