2022
[Chaves da Silva 2022] Anderson Chaves da Silva, Patrick Valduriez, Fabio Porto. Integrating Machine Learning Model Ensembles to the SAVIME Database System. SBBD 2022 – Brazilian Symposium on Databases, SBBD, Buzios, Brazil. pp. 231, 2022.
[Lima 2022] Janio Lima, Pedro Alpis, Rebecca Salles, Luciana Escobar, Fabio Porto, Esther Pacitti, Rafaelli Coutinho, Eduardo Ogasawara. Forward and Backward Inertial Anomaly Detector: A Novel Time Series
Event Detection Method. IEEE International Joint Conference on Neural Networks (IJCNN), 2022.
[Pena 2022] Eduardo H. M. Pena, Fábio Porto, Felix Naumann. Fast Algorithms for Denial Constraint Discovery. Proc. VLDB Endow. 16(4): 684-696, 2022.
[Porto 2022] Fabio Porto, Patrick Valduriez. Data and Machine Learning Model Management with Gypscie. CARLA 2022 – Workshop on HPC and Data Sciences meet Scientific Computing, SCALAC, Porto Alegre, Brazil. pp.1-2, 2022.
[Rosendo 2022a] Daniel Rosendo, Alexandru Costan, Patrick Valduriez, Gabriel Antoniu. Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review. Journal of Parallel and Distributed Computing, Elsevier, 2022, 166, pp.71-94.
[Rosendo 2022b] Daniel Rosendo, Alexandru Costan, Gabriel Antoniu, Patrick Valduriez. Reproducible Performance Optimization of Complex Applications on the Edge-to-Cloud Continuum. BDA Conference, 2022.
[Salles 2022a] Rebecca Salles, Esther Pacitti, Eduardo Bezerra, Fabio Porto, Eduardo Ogasawara. TSPred: A framework for nonstationary time series prediction. Neurocomputing, Elsevier, 467, pp.197-202, 2022.
[Salles 2022b] Rebecca Salles, Esther Pacitti, Eduardo Bezerra, Fabio Porto, Eduardo Ogasawara. TSPred: A framework for nonstationary time series prediction. BDA Conference, 2022.
[Souza 2022] Renan Souza, Leonardo Azevedo, Vítor Lourenço, Elton Soares, Raphael Thiago, Rafael Brandão, Daniel Civitarese, Emilio Brazil, Marcio Moreno, Patrick Valduriez, Marta Mattoso, Renato Cerqueira, Marco Netto. Workflow Provenance in the Lifecycle of Scientific Machine Learning. Concurrency and Computation: Practice and Experience, 34 (14), pp.e6544, 2022.
[Zorrilla 2022] Rocío Zorrilla, Eduardo Ogasawara, Patrick Valduriez, Fabio Porto. A Data-Driven Model Selection Approach to Spatio-Temporal Prediction. SBBD 2022 – Brazilian Symposium on Databases, SBBD, Buzios, Brazil. pp.1-12, 2022.
2021
[Borges 2021] Heraldo Borges, Reza Akbarinia, Florent Masseglia. Anomaly Detection in Time Series. Transactions on Large-Scale Data- and Knowledge-Centered Systems, Springer, 17 pages, In press, 2021.
[Castro 2021] Antonio Castro, Heraldo Borges, Ricardo Campisano, Esther Pacitti, Fabio Porto, Generalização de Mineração de Sequências Restritas no Espaço e no Tempo. SBBD: Simpósio Brasileiro de Banco de Dados, SBC, Online, Brazil. pp. 313-318, 2021.
[Heidsieck 2021] Gaëtan Heidsieck, Daniel de Oliveira, Esther Pacitti, Christophe Pradal, Francois Tardieu, Patrick Valduriez. Cache-aware scheduling of scientific workflows in a multisite cloud. Future Generation Computer Systems (FGCS), pp.172-186, 2021.
[Kunstmann 2021] Liliane Kunstmann, Debora Pina, Filipe Silva, Aline Paes, Patrick Valduriez, Daniel de Oliveira, Marta Mattoso. Online Deep Learning Hyperparameter Tuning based on Provenance Analysis. Journal of Information and Data Management, 12(5), pp.396-414, 2021.
[Pina 2021] Débora Pina, Liliane Kunstmann, Daniel de Oliveira, Patrick Valduriez, Marta Mattoso. Provenance Supporting Hyperparameter Analysis in Deep Neural Networks. International Provenance and Annotation Workshop (IPAW), 2021.
[Rosendo 2021a] Daniel Rosendo, Alexandru Costan, Gabriel Antoniu, Patrick Valduriez. Reproducible Performance Optimization of Complex Applications on the Edge-to-Cloud Continuum. IEEE International Conference on Cluster Computing (Cluster), 2021.
[Rosendo 2021b] Daniel Rosendo, Alexandru Costan, Gabriel Antoniu, Patrick Valduriez. Enabling Reproducible Analysis of Complex Workflows on the Edge-to-Cloud Continuum. BDA conference, 2021.
[Silva 2021a] Rodrigo Silva, Esther Pacitti, Yuri Frota, Daniel de Oliveira. Análise de Desempenho da Distribuição de Workflows Científicos em Nuvens com Restrições de Confidencialidade. Workshop on Computer and Communication Systems Performance (WPerformance 2021), CSBC, Online, Brazil. pp.12, 2021.
[Silva 2021b] Daniel Silva, Esther Pacitti, Aline Paes, Daniel de Oliveira. Provenance-and machine learning-based recommendation of parameter values in scientific workflows. PeerJ Computer Science, PeerJ, 7, pp.e606, 2021.
[Silva 2021c] Rômulo Silva, Debora Pina, Liliane Kunstmann, Daniel de Oliveira, Patrick Valduriez, Alvaro L.G.A. Coutinho, Marta Mattoso. Capturing Provenance to Improve the Model Training of PINNs: first hands-on experiences with Grid5000. Pan-American Congress on Computational Mechanics, CILAMCE-PANACM, p. 1-7, 2021.
[Souza 2021] Renan Souza, Vitor Silva, Alexandre Lima, Daniel de Oliveira, Patrick Valduriez, Marta Mattoso. Distributed in-memory data management for workflow executions. PeerJ Computer Science, PeerJ, 2021.
2020
[Borges 2020a] Heraldo Borges, Murillo Dutra, Amin Bazaz, Rafaelli Coutinho, Fabio Perosi, Fabio Porto, Florent Masseglia, Esther Pacitti, Eduardo Ogasawara. Spatial-time motifs discovery. Intelligent Data Analysis, 24, p. 1121-1140, 2020.
[Borges 2020b] Heraldo Borges, Amin Bazaz, Esther Pacitti, Eduardo Ogasawara. STMotif: Discovery of Motifs in Spatial-Time Series. https://cran.r-project.org/web/packages/STMotif, 2020.
[Castro, 2020] Antonio Castro, Heraldo Borges, Riccardo Campisano, Florent Masseglia, Reza Akbarinia, Esther Pacitti, Fabio Porto, Rafaelli Coutinho, Eduardo Ogasawara, 2020, Generalized Discovery of Tight Space-Time Sequences, Submitted for publication, 2020.
[Lemus 2020] Noel Lemus, Fábio Porto, Yania Souto, Rafael Pereira, Ji Liu, Esther Pacitti, Patrick Valduriez. SUQ$2$: Uncertainty Quantification Queries over Large Spatio-temporal Simulations. IEEE Data Engineering Bulletin 43(1), pp.47-59, 2020.
[Liu 2020] Ji Liu, Noel Moreno Lemus, Esther Pacitti, Fábio Porto, Patrick Valduriez. Parallel Computation of PDFs on Big Spatial Data Using Spark. Distributed and Parallel Databases, Springer, 38, pp.63-100, 2020.
[Lustosa 2020a] Hermano Lustosa, Anderson C. Silva, Daniel N. R. da Silva, Fabio Porto, Patrick Valduriez. SAVIME: An Array DBMS for Simulation Analysis and ML Models Prediction. Submitted for publication, 2020.
[Lustosa 2020b] Hermano Lustosa, Patrick Valduriez, Fabio Porto. Efficient Declarative Array processing in SAVIME. Submitted for publication, 2020.
[Heidsieck 2020a] Gaëtan Heidsieck, Daniel de Oliveira, Esther Pacitti, Christophe Pradal, Francois Tardieu, Patrick Valduriez. Efficient Execution of Scientific Workflows in the Cloud Through Adaptive Caching. Transactions on Large-Scale Data-and Knowledge-Centered Systems (TLDKS), 44, pp.41-66, 2020.
[Heidsieck 2020b] Gaëtan Heidsieck, Daniel de Oliveira, Esther Pacitti, Christophe Pradal, Francois Tardieu, Patrick Valduriez. Distributed Caching of Scientific Workflows in Multisite Cloud. 31st International Conference on Database and Expert Systems Applications (DEXA), pp.51-65, 2020. Best paper award.
[Pina 2020] Débora Pina, Liliane Kunstmann, Daniel de Oliveira, Patrick Valduriez, Marta Mattoso. Uma abordagem para coleta e análise de dados de configurações em redes neurais profundas. Simpósio Brasileiro de Banco de Dados (SBBD), Virtual, Brazil. pp.1-6, 2020.
[Souza 2020a] Renan Souza, Vitor Silva, Alvaro L.G.A. Coutinho, Patrick Valduriez, Marta Mattoso. Data reduction in scientific workflows using provenance monitoring and user steering. Future Generation Computer Systems (FGCS), Elsevier, 110, pp.481-501, 2020.
[Souza 2020b] Renan Souza, Leonardo Guerreiro Azevedo, Vítor Lourenço, Elton F. S. Soares, Raphael Thiago, Rafael Brandão, Daniel Civitarese, Emilio Vital Brazil, Márcio Ferreira Moreno, Patrick Valduriez, Marta Mattoso, Renato Cerqueira, Marco A. S. Netto. Workflow Provenance in the Lifecycle of Scientific Machine Learning. CoRRabs/2010.00330, 2020).
[Silva 2020] Vítor Silva, Vinícius Campos, Thaylon Guedes, José Camata, Daniel de Oliveira, Alvaro Coutinho, Patrick Valduriez, Marta Mattoso. DfAnalyzer: Runtime dataflow analysis tool for Computational Science and Engineering applications. SoftwareX, Elsevier, 12, pp.100592, 2020.
