Ensemble Learning of Run-Time Prediction Models for Data-Intensive Scientific Workflows

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00221325" target="_blank" >RIV/68407700:21230/14:00221325 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/chapter/10.1007/978-3-662-45483-1_7" target="_blank" >http://link.springer.com/chapter/10.1007/978-3-662-45483-1_7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-662-45483-1_7" target="_blank" >10.1007/978-3-662-45483-1_7</a>

Result language
angličtina
Original language name
Ensemble Learning of Run-Time Prediction Models for Data-Intensive Scientific Workflows
Original language description
This paper proposes a novel approach that enables the construction models for predicting task?s running-times of data-intensive scientific workflows. Ensemble Machine Learning techniques are used to produce robust combined models with high predictive accuracy. Information derived from workflow systems and the characteristics and provenance of the data are exploited to guarantee the accuracy of the models. The proposed approach has been tested on Bioinformatics workflows for Gene Expressions Analysis over homogeneous and heterogeneous computing environments.
Czech name
—
Czech description
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Project
<a href="/en/project/MEB111005" target="_blank" >MEB111005: Data Mining over Distribute Computing</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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