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The Collaborative Research Center FONDA

Today’s scientific data analysis very often requires complex Data Analysis Workflows (DAWs) executed over distributed computational infrastructures, e.g., clusters. Much research effort is devoted to the tuning and performance optimization of specific workflows for specific clusters. However, an arg...

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Autores principales: Leser, Ulf, Hilbrich, Marcus, Draxl, Claudia, Eisert, Peter, Grunske, Lars, Hostert, Patrick, Kainmüller, Dagmar, Kao, Odej, Kehr, Birte, Kehrer, Timo, Koch, Christoph, Markl, Volker, Meyerhenke, Henning, Rabl, Tilmann, Reinefeld, Alexander, Reinert, Knut, Ritter, Kerstin, Scheuermann, Björn, Schintke, Florian, Schweikardt, Nicole, Weidlich, Matthias
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8587492/
https://www.ncbi.nlm.nih.gov/pubmed/34786019
http://dx.doi.org/10.1007/s13222-021-00397-5
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author Leser, Ulf
Hilbrich, Marcus
Draxl, Claudia
Eisert, Peter
Grunske, Lars
Hostert, Patrick
Kainmüller, Dagmar
Kao, Odej
Kehr, Birte
Kehrer, Timo
Koch, Christoph
Markl, Volker
Meyerhenke, Henning
Rabl, Tilmann
Reinefeld, Alexander
Reinert, Knut
Ritter, Kerstin
Scheuermann, Björn
Schintke, Florian
Schweikardt, Nicole
Weidlich, Matthias
author_facet Leser, Ulf
Hilbrich, Marcus
Draxl, Claudia
Eisert, Peter
Grunske, Lars
Hostert, Patrick
Kainmüller, Dagmar
Kao, Odej
Kehr, Birte
Kehrer, Timo
Koch, Christoph
Markl, Volker
Meyerhenke, Henning
Rabl, Tilmann
Reinefeld, Alexander
Reinert, Knut
Ritter, Kerstin
Scheuermann, Björn
Schintke, Florian
Schweikardt, Nicole
Weidlich, Matthias
author_sort Leser, Ulf
collection PubMed
description Today’s scientific data analysis very often requires complex Data Analysis Workflows (DAWs) executed over distributed computational infrastructures, e.g., clusters. Much research effort is devoted to the tuning and performance optimization of specific workflows for specific clusters. However, an arguably even more important problem for accelerating research is the reduction of development, adaptation, and maintenance times of DAWs. We describe the design and setup of the Collaborative Research Center (CRC) 1404 “FONDA -– Foundations of Workflows for Large-Scale Scientific Data Analysis”, in which roughly 50 researchers jointly investigate new technologies, algorithms, and models to increase the portability, adaptability, and dependability of DAWs executed over distributed infrastructures. We describe the motivation behind our project, explain its underlying core concepts, introduce FONDA’s internal structure, and sketch our vision for the future of workflow-based scientific data analysis. We also describe some lessons learned during the “making of” a CRC in Computer Science with strong interdisciplinary components, with the aim to foster similar endeavors.
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spelling pubmed-85874922021-11-12 The Collaborative Research Center FONDA Leser, Ulf Hilbrich, Marcus Draxl, Claudia Eisert, Peter Grunske, Lars Hostert, Patrick Kainmüller, Dagmar Kao, Odej Kehr, Birte Kehrer, Timo Koch, Christoph Markl, Volker Meyerhenke, Henning Rabl, Tilmann Reinefeld, Alexander Reinert, Knut Ritter, Kerstin Scheuermann, Björn Schintke, Florian Schweikardt, Nicole Weidlich, Matthias Datenbank Spektrum Community Today’s scientific data analysis very often requires complex Data Analysis Workflows (DAWs) executed over distributed computational infrastructures, e.g., clusters. Much research effort is devoted to the tuning and performance optimization of specific workflows for specific clusters. However, an arguably even more important problem for accelerating research is the reduction of development, adaptation, and maintenance times of DAWs. We describe the design and setup of the Collaborative Research Center (CRC) 1404 “FONDA -– Foundations of Workflows for Large-Scale Scientific Data Analysis”, in which roughly 50 researchers jointly investigate new technologies, algorithms, and models to increase the portability, adaptability, and dependability of DAWs executed over distributed infrastructures. We describe the motivation behind our project, explain its underlying core concepts, introduce FONDA’s internal structure, and sketch our vision for the future of workflow-based scientific data analysis. We also describe some lessons learned during the “making of” a CRC in Computer Science with strong interdisciplinary components, with the aim to foster similar endeavors. Springer Berlin Heidelberg 2021-11-12 2021 /pmc/articles/PMC8587492/ /pubmed/34786019 http://dx.doi.org/10.1007/s13222-021-00397-5 Text en © Gesellschaft für Informatik e.V. and Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Community
Leser, Ulf
Hilbrich, Marcus
Draxl, Claudia
Eisert, Peter
Grunske, Lars
Hostert, Patrick
Kainmüller, Dagmar
Kao, Odej
Kehr, Birte
Kehrer, Timo
Koch, Christoph
Markl, Volker
Meyerhenke, Henning
Rabl, Tilmann
Reinefeld, Alexander
Reinert, Knut
Ritter, Kerstin
Scheuermann, Björn
Schintke, Florian
Schweikardt, Nicole
Weidlich, Matthias
The Collaborative Research Center FONDA
title The Collaborative Research Center FONDA
title_full The Collaborative Research Center FONDA
title_fullStr The Collaborative Research Center FONDA
title_full_unstemmed The Collaborative Research Center FONDA
title_short The Collaborative Research Center FONDA
title_sort collaborative research center fonda
topic Community
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8587492/
https://www.ncbi.nlm.nih.gov/pubmed/34786019
http://dx.doi.org/10.1007/s13222-021-00397-5
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