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lirmm-01400532v1  Poster communications
Vítor SilvaJosé CamataDaniel de OliveiraAlvaro L G A CoutinhoPatrick Valduriez et al.  In Situ Data Steering on Sedimentation Simulation with Provenance Data
SC: High Performance Computing, Networking, Storage and Analysis, Nov 2016, Salt Lake City, United States. International Conference for High Performance Computing, Networking, Storage and Analysis, 2016
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hal-02962579v1  Conference papers
Gaëtan HeidsieckDaniel de OliveiraEsther PacittiChristophe PradalFrancois Tardieu et al.  Distributed Caching of Scientific Workflows in Multisite Cloud
DEXA 2020 - 31st International Conference on Database and Expert Systems Applications, Sep 2020, Bratislava, Slovakia. pp.51-65, ⟨10.1007/978-3-030-59051-2_4⟩
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hal-02969510v1  Journal articles
Gaëtan HeidsieckDaniel de OliveiraEsther PacittiChristophe PradalFrancois Tardieu et al.  Efficient Execution of Scientific Workflows in the Cloud Through Adaptive Caching
Transactions on Large-Scale Data- and Knowledge-Centered Systems, Springer Berlin / Heidelberg, 2020, pp.41-66. ⟨10.1007/978-3-662-62271-1_2⟩
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lirmm-01868574v1  Conference papers
Daniel SilvaAline PaesEsther PacittiDaniel de Oliveira. FReeP: towards parameter recommendation in scientific workflows using preference learning
33rd Brazilian Symposium on Databases (SBBD 2018), Aug 2018, Rio de Janeiro, Brazil. pp.211-216
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hal-02174445v1  Conference papers
Gaëtan HeidsieckDaniel de OliveiraEsther PacittiChristophe PradalFrancois Tardieu et al.  Adaptive Caching for Data-Intensive Scientific Workflows in the Cloud
DEXA 2019 - 30th International Conference on Database and Expert Systems Applications, Aug 2019, Linz, Austria. pp.452-466, ⟨10.1007/978-3-030-27618-8_33⟩
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lirmm-03443548v1  Conference papers
Rômulo M. SilvaDébora PinaLiliane KunstmannDaniel de OliveiraPatrick Valduriez et al.  Capturing Provenance to Improve the Model Training of PINNs: first handon experiences with Grid5000
42nd Ibero-Latin-American Congress on Computational Methods in Engineering and 3rd Pan American Congress on Computational Mechanics, Nov 2021, Rio de Janeiro, Brazil. pp.1-7
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lirmm-01409092v1  Conference papers
Ji LiuEsther PacittiPatrick ValduriezDaniel de OliveiraMarta Mattoso. Scientific Workflow Execution with Multiple Objectives in Multisite Clouds
BDA: Gestion de Données — Principes, Technologies et Applications, LIAS / ISAE-ENSMA, Poitiers, Nov 2016, Poitiers, France
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lirmm-03108922v1  Conference papers
Vítor SilvaRenan SouzaJose CamataDaniel de OliveiraPatrick Valduriez et al.  Capturing Provenance for Runtime Data Analysis in Computational Science and Engineering Applications
7th International Provenance and Annotation Workshop (IPAW), Jul 2018, London, United Kingdom. pp.183-187, ⟨10.1007/978-3-319-98379-0_15⟩
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lirmm-03443660v1  Journal articles
Liliane KunstmannDébora PinaFilipe SilvaAline PaesPatrick Valduriez et al.  Online Deep Learning Hyperparameter Tuning based on Provenance Analysis
Journal of Information and Data Management, Brazilian Computer Society, 2021, 12 (5), pp.396-414. ⟨10.5753/jidm.2021.1924⟩
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lirmm-02969506v1  Conference papers
Débora PinaLiliane KunstmannDaniel de OliveiraPatrick ValduriezMarta Mattoso. Uma abordagem para coleta e análise de dados de configurações em redes neurais profundas
SBBD 2020 - 35ª Simpósio Brasileiro de Banco de Dados, Sep 2020, Virtual, Brazil. pp.1-6
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lirmm-03324873v1  Conference papers
Débora PinaLiliane KunstmannDaniel de OliveiraPatrick ValduriezMarta Mattoso. Provenance Supporting Hyperparameter Analysis in Deep Neural Networks
IPAW 2020-2021 - 8th and 9th International Provenance and Annotation Workshop, Jul 2021, London, United Kingdom. pp.20-38, ⟨10.1007/978-3-030-80960-7_2⟩
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hal-00640431v1  Journal articles
Eduardo OgasawaraDaniel de OliveiraPatrick ValduriezJonas DiasFabio Porto et al.  An Algebraic Approach for Data-Centric Scientific Workflows
Proceedings of the VLDB Endowment (PVLDB), VLDB Endowment, 2011, 4 (11), pp.1328-1339
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lirmm-01654914v1  Conference papers
José CamataJosé M CelaDanilo CostaAlvaro Lga CoutinhoDaniel Fernández-Galisteo et al.  Enhancing Energy Production with Exascale HPC Methods
CARLA: Latin American High Performance Computing Conference, Aug 2016, Mexico City, Mexico. pp.233-246, ⟨10.1007/978-3-319-57972-6_17⟩