Periódicos
[1] A. Vasconcelos, J. Monsores, T. Almeida, L. Quadros, E. Ogasawara, and J. Quadros, “Applying Gestalt approach as a method for teaching computer science practice in the classroom: A case study in primary schools in Brazil,” Education and Information Technologies, vol. 28, no. 2, pp. 2383–2403, 2023, doi: 10.1007/s10639-022-11278-z.
[2] R. Salles et al., “TSPredIT: Integrated Tuning of Data Preprocessing and Time Series Prediction Models,” Transactions on Large-Scale Data- and Knowledge-Centered Systems LIV: Special Issue on Data Management – Principles, Technologies, and Applications, vol. 14160 LNCS, pp. 41–55, 2023, doi: 10.1007/978-3-662-68014-8_2.
[3] C. Gea, L. Vereda, and E. Ogasawara, “Detection of Uncertainty Events in the Brazilian Economic and Financial Time Series,” Comput Econ, 2023, doi: 10.1007/s10614-023-10483-3.
[4] P. Elias, Jr. Campos H. D. S., E. Ogasawara, and L. G. P. Murta, “Towards accurate recommendations of merge conflicts resolution strategies,” Information and Software Technology, vol. 164, 2023, doi: 10.1016/j.infsof.2023.107332.
[5] F. P. G. de Sá, R. C. de Coutinho, E. Ogasawara, D. Brandão, and R. F. Toso, “Wind turbine fault detection: a semi-supervised learning approach with two different dimensionality reduction techniques,” International Journal of Innovative Computing and Applications, vol. 14, no. 1–2, pp. 67–77, 2023, doi: 10.1504/IJICA.2023.129359.
[6] F. Carvalho, F. P. Junior, E. Ogasawara, L. Ferrari, and G. Guedes, “Evaluation of the Brazilian Portuguese version of linguistic inquiry and word count 2015 (BP-LIWC2015),” Language Resources and Evaluation, 2023, doi: 10.1007/s10579-023-09647-2.
[7] R. Akbarinia et al., “Life Science Workflow Services (LifeSWS): Motivations and Architecture,” Transactions on Large-Scale Data- and Knowledge-Centered Systems LV, vol. 14280 LNCS, pp. 1–24, 2023, doi: 10.1007/978-3-662-68100-8_1.
[8] C. Teixeira et al., “A horizontal partitioning-based method for frequent pattern mining in transport timetable,” Expert Systems, vol. 39, no. 2. 2022. doi: 10.1111/exsy.12881.
[9] R. Salles, E. Pacitti, E. Bezerra, F. Porto, and E. Ogasawara, “TSPred: A framework for nonstationary time series prediction,” Neurocomputing, vol. 467. pp. 197–202, 2022. doi: 10.1016/j.neucom.2021.09.067.
[10] F. Porto et al., “Machine Learning Approaches to Extreme Weather Events Forecast in Urban Areas: Challenges and Initial Results,” Supercomputing Frontiers and Innovations, vol. 9, no. 1. pp. 49–73, 2022. doi: 10.14529/jsfi220104.
[11] L. Giusti, L. Carvalho, A. T. Gomes, R. Coutinho, J. Soares, and E. Ogasawara, “Analyzing flight delay prediction under concept drift,” Evolving Systems. 2022. doi: 10.1007/s12530-021-09415-z.
[12] B. dos Santos de Assis, E. Ogasawara, R. Barbastefano, and D. Carvalho, “Frequent pattern mining augmented by social network parameters for measuring graduation and dropout time factors: A case study on a production engineering course,” Socio-Economic Planning Sciences, vol. 81. 2022. doi: 10.1016/j.seps.2021.101200.
[13] B. Paixão et al., “Estimation of COVID-19 Under-Reporting in the Brazilian States Through SARI,” New Generation Computing, vol. 39, no. 3–4. pp. 623–645, 2021. doi: 10.1007/s00354-021-00125-3.
[14] L. Escobar et al., “Evaluating Temporal Bias in Time Series Event Detection Methods,” Journal of Information and Data Management, vol. 12, no. 3, Art. no. 3, Oct. 2021, doi: 10.5753/jidm.2021.1968.
[15] R. Castro, Y. M. Souto, E. Ogasawara, F. Porto, and E. Bezerra, “STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for weather forecasting,” Neurocomputing, vol. 426. pp. 285–298, 2021. doi: 10.1016/j.neucom.2020.09.060.
[16] L. Carvalho et al., “On the relevance of data science for flight delay research: a systematic review,” Transport Reviews, vol. 41, no. 4. pp. 499–528, 2021. doi: 10.1080/01441647.2020.1861123.
[17] L. Baroni et al., “Neonatal mortality rates in Brazilian municipalities: from 1996 to 2017,” BMC Research Notes, vol. 14, no. 1. 2021. doi: 10.1186/s13104-020-05441-3.
[18] F. Paschoal Júnior, G. V. S. Ribeiro, L. M. de A. Daquer, R. C. Mauro, E. S. Ogasawara, and N. F. F. Ebecken, “Physical activity level of facebook users; [Nível de atividade física dos usuários do facebook]; [Nivel de actividad física de los usuarios de facebook],” Revista Brasileira de Medicina do Esporte, vol. 26, no. 6. pp. 517–522, 2020. doi: 10.1590/1517-869220202606179014.
[19] R. Guimaraes Rodrigues, K. Tavares Rodrigues, R. Reis Gomes, L. Ferrari, E. Ogasawara, and G. Paiva Guedes, “BRAPT: A New Metric for Translation Evaluation Based on Psycholinguistic Perspectives,” IEEE Latin America Transactions, vol. 18, no. 7. pp. 1264–1271, 2020. doi: 10.1109/TLA.2020.9099768.
[20] J. Cardoso et al., “Supporting the Learning of Evolution Theory Using an Educational Simulator,” IEEE Transactions on Learning Technologies, vol. 13, no. 2. pp. 417–424, 2020. doi: 10.1109/TLT.2019.2911613.
[21] H. Borges et al., “Spatial-time motifs discovery,” Intelligent Data Analysis, vol. 24, no. 5. pp. 1121–1140, 2020. doi: 10.3233/IDA-194759.
[22] L. Baroni et al., “An analysis of malaria in the Brazilian Legal Amazon using divergent association rules,” Journal of Biomedical Informatics, vol. 108. 2020. doi: 10.1016/j.jbi.2020.103512.
[23] L. Baroni et al., “An integrated dataset of malaria notifications in the Legal Amazon,” BMC Research Notes, vol. 13, no. 1. 2020. doi: 10.1186/s13104-020-05109-y.
[24] R. Salles, K. Belloze, F. Porto, P. H. Gonzalez, and E. Ogasawara, “Nonstationary time series transformation methods: An experimental review,” Knowledge-Based Systems, vol. 164. pp. 274–291, 2019. doi: 10.1016/j.knosys.2018.10.041.
[25] A. Marinho et al., “Deriving scientific workflows from algebraic experiment lines: A practical approach,” Future Generation Computer Systems, vol. 68. pp. 111–127, 2017. doi: 10.1016/j.future.2016.08.016.
[26] A. Sternberg, D. Carvalho, L. Murta, J. Soares, and E. Ogasawara, “An analysis of Brazilian flight delays based on frequent patterns,” Transportation Research Part E: Logistics and Transportation Review, vol. 95. pp. 282–298, 2016. doi: 10.1016/j.tre.2016.09.013.
[27] R. Salles, P. Mattos, A.-M. D. Iorgulescu, E. Bezerra, L. Lima, and E. Ogasawara, “Evaluating temporal aggregation for predicting the sea surface temperature of the Atlantic Ocean,” Ecological Informatics, vol. 36. pp. 94–105, 2016. doi: 10.1016/j.ecoinf.2016.10.004.
[28] G. P. Guedes, E. Ogasawara, E. Bezerra, and G. Xexeo, “Discovering top-k non-redundant clusterings in attributed graphs,” Neurocomputing, vol. 210. pp. 45–54, 2016. doi: 10.1016/j.neucom.2015.10.145.
[29] L. Pimentel, K. Belloze, J. Soares, E. Ogasawara, and R. Mauro, “Uma ferramenta para planejamento de estudos para concursos,” Revista Brasileira de Computação Aplicada, vol. 7, no. 3, pp. 17–30, Oct. 31, 2015.
[30] M. Mattoso et al., “Dynamic steering of HPC scientific workflows: A survey,” Future Generation Computer Systems, vol. 46. pp. 100–113, 2015. doi: 10.1016/j.future.2014.11.017.
[31] G. P. G. e Silva, E. Bezerra, E. Ogasawara, and G. Xexeo, “MAM: Método para Agrupamentos Múltiplos em Redes Sociais Online Baseado em Emoções, Personalidades e Textos,” iSys – Brazilian Journal of Information Systems, vol. 7, no. 3, Art. no. 3, Nov. 2014, doi: 10.5753/isys.2014.256.
[32] L. Mosquera, E. Ogasawara, R. Barbastefano, and E. Bezerra, “Proposta de Modelo de Avaliação de Formas de Adoção e Acompanhamento de Ferramentas de Redes Sociais Corporativas,” Sistemas & Gestão, vol. 9, no. 4, Art. no. 4, Dec. 2014, doi: 10.7177/sg.2014.V9.N4.A9.
[33] J. R. de T. Quadros et al., “Construção de ambiente para desenvolvimento de jogos educacionais baseados em interface de gestos,” Revista Brasileira de Computação Aplicada, vol. 5, no. 2, pp. 110–119, Sep. 19, 2013.
[34] E. Ogasawara et al., “Chiron: A parallel engine for algebraic scientific workflows,” Concurrency and Computation: Practice and Experience, vol. 25, no. 16. pp. 2327–2341, 2013. doi: 10.1002/cpe.3032.
[35] E. Ogasawara et al., “A forecasting method for fertilizers consumption in Brazil,” International Journal of Agricultural and Environmental Information Systems, vol. 4, no. 2. pp. 23–36, 2013. doi: 10.4018/jaeis.2013040103.
[36] K. A. C. S. Ocaña, D. De Oliveira, J. Dias, E. Ogasawara, and M. Mattoso, “Designing a parallel cloud based comparative genomics workflow to improve phylogenetic analyses,” Future Generation Computer Systems, vol. 29, no. 8. pp. 2205–2219, 2013. doi: 10.1016/j.future.2013.04.005.
[37] J. Gonçalves, D. de Oliveira, K. Ocaña, E. Ogasawara, J. Dias, and M. Mattoso, “Performance Analysis of Data Filtering in Scientific Workflows,” Journal of Information and Data Management, vol. 4, no. 1, Art. no. 1, Jun. 2013, doi: 10.5753/jidm.2013.1466.
[38] D. De Oliveira et al., “Performance evaluation of parallel strategies in public clouds: A study with phylogenomic workflows,” Future Generation Computer Systems, vol. 29, no. 7. pp. 1816–1825, 2013. doi: 10.1016/j.future.2012.12.019.
[39] D. de Oliveira, E. Ogasawara, J. Dias, F. Baião, and M. Mattoso, “Ontology-based Semi-automatic Workflow Composition,” Journal of Information and Data Management, vol. 3, no. 1, Art. no. 1, Jul. 2012, doi: 10.5753/jidm.2012.1434.
[40] A. Marinho et al., “ProvManager: A provenance management system for scientific workflows,” Concurrency and Computation: Practice and Experience, vol. 24, no. 13. pp. 1513–1530, 2012. doi: 10.1002/cpe.1870.
[41] G. Guerra et al., “Uncertainty Quantification in Computational Predictive Models for Fluid Dynamics Using a Workflow Management Engine,” IJUQ, vol. 2, no. 1, 2012, doi: 10.1615/Int.J.UncertaintyQuantification.v2.i1.50.
[42] D. De Oliveira, E. Ogasawara, K. Ocaña, F. Baião, and M. Mattoso, “An adaptive parallel execution strategy for cloud-based scientific workflows,” Concurrency and Computation: Practice and Experience, vol. 24, no. 13. pp. 1531–1550, 2012. doi: 10.1002/cpe.1880.
[43] V. Silva et al., “Similarity-based workflow clustering,” Journal of Computational Interdisciplinary Sciences, vol. 2, no. 1, 2011.
[44] E. Ogasawara, D. de Oliveira, P. Valduriez, J. Dias, F. Porto, and M. Mattoso, “An algebraic approach for data-centric scientific workflows,” Proceedings of the VLDB Endowment, vol. 4, no. 12. pp. 1328–1339, 2011.
[45] F. Coutinho et al., “Many task computing for orthologous genes identification in protozoan genomes using Hydra,” Concurrency and Computation: Practice and Experience, vol. 23, no. 17. pp. 2326–2337, 2011. doi: 10.1002/cpe.1786.
[46] M. Mattoso et al., “Towards supporting the life cycle of large scale scientific experiments,” International Journal of Business Process Integration and Management, vol. 5, no. 1. pp. 79–92, 2010. doi: 10.1504/IJBPIM.2010.033176.
[2] R. Salles et al., “TSPredIT: Integrated Tuning of Data Preprocessing and Time Series Prediction Models,” Transactions on Large-Scale Data- and Knowledge-Centered Systems LIV: Special Issue on Data Management – Principles, Technologies, and Applications, vol. 14160 LNCS, pp. 41–55, 2023, doi: 10.1007/978-3-662-68014-8_2.
[3] C. Gea, L. Vereda, and E. Ogasawara, “Detection of Uncertainty Events in the Brazilian Economic and Financial Time Series,” Comput Econ, 2023, doi: 10.1007/s10614-023-10483-3.
[4] P. Elias, Jr. Campos H. D. S., E. Ogasawara, and L. G. P. Murta, “Towards accurate recommendations of merge conflicts resolution strategies,” Information and Software Technology, vol. 164, 2023, doi: 10.1016/j.infsof.2023.107332.
[5] F. P. G. de Sá, R. C. de Coutinho, E. Ogasawara, D. Brandão, and R. F. Toso, “Wind turbine fault detection: a semi-supervised learning approach with two different dimensionality reduction techniques,” International Journal of Innovative Computing and Applications, vol. 14, no. 1–2, pp. 67–77, 2023, doi: 10.1504/IJICA.2023.129359.
[6] F. Carvalho, F. P. Junior, E. Ogasawara, L. Ferrari, and G. Guedes, “Evaluation of the Brazilian Portuguese version of linguistic inquiry and word count 2015 (BP-LIWC2015),” Language Resources and Evaluation, 2023, doi: 10.1007/s10579-023-09647-2.
[7] R. Akbarinia et al., “Life Science Workflow Services (LifeSWS): Motivations and Architecture,” Transactions on Large-Scale Data- and Knowledge-Centered Systems LV, vol. 14280 LNCS, pp. 1–24, 2023, doi: 10.1007/978-3-662-68100-8_1.
[8] C. Teixeira et al., “A horizontal partitioning-based method for frequent pattern mining in transport timetable,” Expert Systems, vol. 39, no. 2. 2022. doi: 10.1111/exsy.12881.
[9] R. Salles, E. Pacitti, E. Bezerra, F. Porto, and E. Ogasawara, “TSPred: A framework for nonstationary time series prediction,” Neurocomputing, vol. 467. pp. 197–202, 2022. doi: 10.1016/j.neucom.2021.09.067.
[10] F. Porto et al., “Machine Learning Approaches to Extreme Weather Events Forecast in Urban Areas: Challenges and Initial Results,” Supercomputing Frontiers and Innovations, vol. 9, no. 1. pp. 49–73, 2022. doi: 10.14529/jsfi220104.
[11] L. Giusti, L. Carvalho, A. T. Gomes, R. Coutinho, J. Soares, and E. Ogasawara, “Analyzing flight delay prediction under concept drift,” Evolving Systems. 2022. doi: 10.1007/s12530-021-09415-z.
[12] B. dos Santos de Assis, E. Ogasawara, R. Barbastefano, and D. Carvalho, “Frequent pattern mining augmented by social network parameters for measuring graduation and dropout time factors: A case study on a production engineering course,” Socio-Economic Planning Sciences, vol. 81. 2022. doi: 10.1016/j.seps.2021.101200.
[13] B. Paixão et al., “Estimation of COVID-19 Under-Reporting in the Brazilian States Through SARI,” New Generation Computing, vol. 39, no. 3–4. pp. 623–645, 2021. doi: 10.1007/s00354-021-00125-3.
[14] L. Escobar et al., “Evaluating Temporal Bias in Time Series Event Detection Methods,” Journal of Information and Data Management, vol. 12, no. 3, Art. no. 3, Oct. 2021, doi: 10.5753/jidm.2021.1968.
[15] R. Castro, Y. M. Souto, E. Ogasawara, F. Porto, and E. Bezerra, “STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for weather forecasting,” Neurocomputing, vol. 426. pp. 285–298, 2021. doi: 10.1016/j.neucom.2020.09.060.
[16] L. Carvalho et al., “On the relevance of data science for flight delay research: a systematic review,” Transport Reviews, vol. 41, no. 4. pp. 499–528, 2021. doi: 10.1080/01441647.2020.1861123.
[17] L. Baroni et al., “Neonatal mortality rates in Brazilian municipalities: from 1996 to 2017,” BMC Research Notes, vol. 14, no. 1. 2021. doi: 10.1186/s13104-020-05441-3.
[18] F. Paschoal Júnior, G. V. S. Ribeiro, L. M. de A. Daquer, R. C. Mauro, E. S. Ogasawara, and N. F. F. Ebecken, “Physical activity level of facebook users; [Nível de atividade física dos usuários do facebook]; [Nivel de actividad física de los usuarios de facebook],” Revista Brasileira de Medicina do Esporte, vol. 26, no. 6. pp. 517–522, 2020. doi: 10.1590/1517-869220202606179014.
[19] R. Guimaraes Rodrigues, K. Tavares Rodrigues, R. Reis Gomes, L. Ferrari, E. Ogasawara, and G. Paiva Guedes, “BRAPT: A New Metric for Translation Evaluation Based on Psycholinguistic Perspectives,” IEEE Latin America Transactions, vol. 18, no. 7. pp. 1264–1271, 2020. doi: 10.1109/TLA.2020.9099768.
[20] J. Cardoso et al., “Supporting the Learning of Evolution Theory Using an Educational Simulator,” IEEE Transactions on Learning Technologies, vol. 13, no. 2. pp. 417–424, 2020. doi: 10.1109/TLT.2019.2911613.
[21] H. Borges et al., “Spatial-time motifs discovery,” Intelligent Data Analysis, vol. 24, no. 5. pp. 1121–1140, 2020. doi: 10.3233/IDA-194759.
[22] L. Baroni et al., “An analysis of malaria in the Brazilian Legal Amazon using divergent association rules,” Journal of Biomedical Informatics, vol. 108. 2020. doi: 10.1016/j.jbi.2020.103512.
[23] L. Baroni et al., “An integrated dataset of malaria notifications in the Legal Amazon,” BMC Research Notes, vol. 13, no. 1. 2020. doi: 10.1186/s13104-020-05109-y.
[24] R. Salles, K. Belloze, F. Porto, P. H. Gonzalez, and E. Ogasawara, “Nonstationary time series transformation methods: An experimental review,” Knowledge-Based Systems, vol. 164. pp. 274–291, 2019. doi: 10.1016/j.knosys.2018.10.041.
[25] A. Marinho et al., “Deriving scientific workflows from algebraic experiment lines: A practical approach,” Future Generation Computer Systems, vol. 68. pp. 111–127, 2017. doi: 10.1016/j.future.2016.08.016.
[26] A. Sternberg, D. Carvalho, L. Murta, J. Soares, and E. Ogasawara, “An analysis of Brazilian flight delays based on frequent patterns,” Transportation Research Part E: Logistics and Transportation Review, vol. 95. pp. 282–298, 2016. doi: 10.1016/j.tre.2016.09.013.
[27] R. Salles, P. Mattos, A.-M. D. Iorgulescu, E. Bezerra, L. Lima, and E. Ogasawara, “Evaluating temporal aggregation for predicting the sea surface temperature of the Atlantic Ocean,” Ecological Informatics, vol. 36. pp. 94–105, 2016. doi: 10.1016/j.ecoinf.2016.10.004.
[28] G. P. Guedes, E. Ogasawara, E. Bezerra, and G. Xexeo, “Discovering top-k non-redundant clusterings in attributed graphs,” Neurocomputing, vol. 210. pp. 45–54, 2016. doi: 10.1016/j.neucom.2015.10.145.
[29] L. Pimentel, K. Belloze, J. Soares, E. Ogasawara, and R. Mauro, “Uma ferramenta para planejamento de estudos para concursos,” Revista Brasileira de Computação Aplicada, vol. 7, no. 3, pp. 17–30, Oct. 31, 2015.
[30] M. Mattoso et al., “Dynamic steering of HPC scientific workflows: A survey,” Future Generation Computer Systems, vol. 46. pp. 100–113, 2015. doi: 10.1016/j.future.2014.11.017.
[31] G. P. G. e Silva, E. Bezerra, E. Ogasawara, and G. Xexeo, “MAM: Método para Agrupamentos Múltiplos em Redes Sociais Online Baseado em Emoções, Personalidades e Textos,” iSys – Brazilian Journal of Information Systems, vol. 7, no. 3, Art. no. 3, Nov. 2014, doi: 10.5753/isys.2014.256.
[32] L. Mosquera, E. Ogasawara, R. Barbastefano, and E. Bezerra, “Proposta de Modelo de Avaliação de Formas de Adoção e Acompanhamento de Ferramentas de Redes Sociais Corporativas,” Sistemas & Gestão, vol. 9, no. 4, Art. no. 4, Dec. 2014, doi: 10.7177/sg.2014.V9.N4.A9.
[33] J. R. de T. Quadros et al., “Construção de ambiente para desenvolvimento de jogos educacionais baseados em interface de gestos,” Revista Brasileira de Computação Aplicada, vol. 5, no. 2, pp. 110–119, Sep. 19, 2013.
[34] E. Ogasawara et al., “Chiron: A parallel engine for algebraic scientific workflows,” Concurrency and Computation: Practice and Experience, vol. 25, no. 16. pp. 2327–2341, 2013. doi: 10.1002/cpe.3032.
[35] E. Ogasawara et al., “A forecasting method for fertilizers consumption in Brazil,” International Journal of Agricultural and Environmental Information Systems, vol. 4, no. 2. pp. 23–36, 2013. doi: 10.4018/jaeis.2013040103.
[36] K. A. C. S. Ocaña, D. De Oliveira, J. Dias, E. Ogasawara, and M. Mattoso, “Designing a parallel cloud based comparative genomics workflow to improve phylogenetic analyses,” Future Generation Computer Systems, vol. 29, no. 8. pp. 2205–2219, 2013. doi: 10.1016/j.future.2013.04.005.
[37] J. Gonçalves, D. de Oliveira, K. Ocaña, E. Ogasawara, J. Dias, and M. Mattoso, “Performance Analysis of Data Filtering in Scientific Workflows,” Journal of Information and Data Management, vol. 4, no. 1, Art. no. 1, Jun. 2013, doi: 10.5753/jidm.2013.1466.
[38] D. De Oliveira et al., “Performance evaluation of parallel strategies in public clouds: A study with phylogenomic workflows,” Future Generation Computer Systems, vol. 29, no. 7. pp. 1816–1825, 2013. doi: 10.1016/j.future.2012.12.019.
[39] D. de Oliveira, E. Ogasawara, J. Dias, F. Baião, and M. Mattoso, “Ontology-based Semi-automatic Workflow Composition,” Journal of Information and Data Management, vol. 3, no. 1, Art. no. 1, Jul. 2012, doi: 10.5753/jidm.2012.1434.
[40] A. Marinho et al., “ProvManager: A provenance management system for scientific workflows,” Concurrency and Computation: Practice and Experience, vol. 24, no. 13. pp. 1513–1530, 2012. doi: 10.1002/cpe.1870.
[41] G. Guerra et al., “Uncertainty Quantification in Computational Predictive Models for Fluid Dynamics Using a Workflow Management Engine,” IJUQ, vol. 2, no. 1, 2012, doi: 10.1615/Int.J.UncertaintyQuantification.v2.i1.50.
[42] D. De Oliveira, E. Ogasawara, K. Ocaña, F. Baião, and M. Mattoso, “An adaptive parallel execution strategy for cloud-based scientific workflows,” Concurrency and Computation: Practice and Experience, vol. 24, no. 13. pp. 1531–1550, 2012. doi: 10.1002/cpe.1880.
[43] V. Silva et al., “Similarity-based workflow clustering,” Journal of Computational Interdisciplinary Sciences, vol. 2, no. 1, 2011.
[44] E. Ogasawara, D. de Oliveira, P. Valduriez, J. Dias, F. Porto, and M. Mattoso, “An algebraic approach for data-centric scientific workflows,” Proceedings of the VLDB Endowment, vol. 4, no. 12. pp. 1328–1339, 2011.
[45] F. Coutinho et al., “Many task computing for orthologous genes identification in protozoan genomes using Hydra,” Concurrency and Computation: Practice and Experience, vol. 23, no. 17. pp. 2326–2337, 2011. doi: 10.1002/cpe.1786.
[46] M. Mattoso et al., “Towards supporting the life cycle of large scale scientific experiments,” International Journal of Business Process Integration and Management, vol. 5, no. 1. pp. 79–92, 2010. doi: 10.1504/IJBPIM.2010.033176.
Conferências
[1] M. Pedroso et al., “Data Science Platform Applied to Health in Contribution to the Brazilian Unified Health System,” in CEUR Workshop Proceedings, 2023.
[2] A. Castro et al., “GSTSM Package: Finding Frequent Sequences in Constrained Space and Time,” in BDA 2023 : 39ème Conférence sur la Gestion de Données – Principes, Technologies et Applications, BDA, Sep. 2023, pp. 1–2.
[3] B. Capistrano et al., “Desafios na Predição do Consumo de Pesticidas em Escala Global Usando Aprendizado de Máquina,” in Anais do Brazilian e-Science Workshop (BreSci), SBC, Sep. 2023, pp. 33–38. doi: 10.5753/bresci.2023.233831.
[4] H. Borges, A. Castro, R. Coutinho, F. Porto, E. Pacitti, and E. Ogasawara, “STMotif Explorer: A Tool for Spatiotemporal Motif Analysis,” in Anais Estendidos do Simpósio Brasileiro de Banco de Dados (SBBD), SBC, Sep. 2023, pp. 114–119. doi: 10.5753/sbbd_estendido.2023.233371.
[5] R. Zorrilla, E. Ogasawara, P. Valduriez, and F. Porto, “A Data-Driven Model Selection Approach to Spatio-Temporal Prediction,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), SBC, Sep. 2022, pp. 1–12. doi: 10.5753/sbbd.2022.224638.
[6] C. Pacheco et al., “Exploring Data Preprocessing and Machine Learning Methods for Forecasting Worldwide Fertilizers Consumption,” Proceedings of the International Joint Conference on Neural Networks, vol. 2022-July. 2022. doi: 10.1109/IJCNN55064.2022.9892325.
[7] J. Lima et al., “Forward and Backward Inertial Anomaly Detector: A Novel Time Series Event Detection Method,” Proceedings of the International Joint Conference on Neural Networks, vol. 2022-July. pp. 1–8, 2022. doi: 10.1109/IJCNN55064.2022.9892088.
[8] J. Lima et al., “Towards a cloud-based framework for online and integrated event detection,” in Anais Estendidos do Simpósio Brasileiro de Banco de Dados (SBBD), SBC, Sep. 2022, pp. 199–202. doi: 10.5753/sbbd_estendido.2022.21865.
[9] L. Izaú et al., “Towards Robust Cluster-Based Hyperparameter Optimization,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), SBC, Sep. 2022, pp. 439–444. doi: 10.5753/sbbd.2022.224330.
[10] A. J. M. da Fonseca, F. Porto, M. Ferro, E. Ogasawara, and E. Bezerra, “Analysis of precipitation data in Rio de Janeiro city using Extreme Value Theory,” in Anais Estendidos do Simpósio Brasileiro de Banco de Dados (SBBD), SBC, Sep. 2022, pp. 193–198. doi: 10.5753/sbbd_estendido.2022.21864.
[11] M. Ferro, E. Bezerra, E. Ogasawara, N. Moraes, and F. Porto, “Towards a Definition for Extreme Weather Events in Rio de Janeiro City,” in Anais Estendidos do Simpósio Brasileiro de Banco de Dados (SBBD), SBC, Sep. 2022, pp. 181–186. doi: 10.5753/sbbd_estendido.2022.21862.
[12] R. B. de Castro, V. C. Monteiro, R. Coutinho, H. Borges, and E. Ogasawara, “Identification of the North Brazil Current through spatial motifs in fixed time slices,” in Anais do Brazilian e-Science Workshop (BreSci), SBC, Jul. 2022, pp. 17–24. doi: 10.5753/bresci.2022.222622.
[13] C. Teixeira, L. Giusti, J. Soares, J. dos Santos, G. Amorim, and E. Ogasawara, “Integrated Dataset of Brazilian Flights,” in Anais do Brazilian e-Science Workshop (BreSci), SBC, Jul. 2021, pp. 89–96. doi: 10.5753/bresci.2021.15793.
[14] G. Souto, B. Capistrano, M. Matias, J. Soares, E. Ogasawara, and L. Giusti, “Avaliação dos diferentes tipos de redes LSTM para predição de ações na bolsa de valores,” in Anais da Escola Regional de Informática do Rio de Janeiro (ERI-RJ), SBC, Nov. 2021, pp. 65–71. doi: 10.5753/eri-rj.2021.18776.
[15] R. P. Salles, E. Ogasawara, and P. González, “Benchmarking Nonstationary Time Series Prediction,” in Anais Estendidos do Simpósio Brasileiro de Banco de Dados (SBBD), SBC, Oct. 2021, pp. 177–182. doi: 10.5753/sbbd_estendido.2021.18182.
[16] R. Pereira et al., “DJEnsemble: A Cost-Based Selection and Allocation of a Disjoint Ensemble of Spatio-Temporal Models,” ACM International Conference Proceeding Series. Association for Computing Machinery, pp. 226–231, 2021. doi: 10.1145/3468791.3468806.
[17] M. Mello, V. Belloni, F. Vasconcellos, J. Soares, E. Ogasawara, and L. Giusti, “Funções Executivas e Idade Relativa como Preditores de Sucesso no Futebol,” in Anais da Escola Regional de Informática do Rio de Janeiro (ERI-RJ), SBC, Nov. 2021, pp. 111–118. doi: 10.5753/eri-rj.2021.18782.
[18] C. Gea, J. Lima, E. Bezerra, and E. Ogasawara, “Análise de métodos de tratamento de outliers para predição dos retornos de índices de ações negociados em bolsa,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), SBC, Oct. 2021, pp. 277–282. doi: 10.5753/sbbd.2021.17885.
[19] M. B. Ferreira, M. Amorim, E. Ogasawara, and R. Barbastefano, “A interdisciplinaridade no desempenho da nota de matemática: um olhar para evolução do processo de ensino por meio de modelos regressivos,” in Anais da Escola Regional de Informática do Rio de Janeiro (ERI-RJ), SBC, Nov. 2021, pp. 41–48. doi: 10.5753/eri-rj.2021.18773.
[20] J. H. L. Fabian, A. T. A. Gomes, and E. Ogasawara, “Estimating the Execution Time of the Coupled Stage in Multiscale Numerical Simulations,” Communications in Computer and Information Science, vol. 1327. pp. 86–100, 2021. doi: 10.1007/978-3-030-68035-0_7.
[21] A. Castro et al., “Generalização de Mineração de Sequências Restritas no Espaço e no Tempo,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), SBC, Oct. 2021, pp. 313–318. doi: 10.5753/sbbd.2021.17891.
[22] R. Campos, H. Benini, J. dos Santos, E. Ogasawara, and F. Marques, “Classificação da Avaliação de Imersão em Aplicações Multissensoriais,” in Anais da Escola Regional de Informática do Rio de Janeiro (ERI-RJ), SBC, Nov. 2021, pp. 127–130. doi: 10.5753/eri-rj.2021.18785.
[23] C. Barros, R. Salles, E. Ogasawara, G. Guizzardi, and F. Porto, “Requirements for an ontology of digital twins,” CEUR Workshop Proceedings, vol. 2941. 2021.
[24] R. Salles et al., “Harbinger: Um framework para integração e análise de métodos de detecção de eventos em séries temporais,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), SBC, Sep. 2020, pp. 73–84. doi: 10.5753/sbbd.2020.13626.
[25] J. Fabian, A. Gomes, and E. Ogasawara, “Estimating the execution time of fully-online multiscale numerical simulations,” in Anais do Simpósio em Sistemas Computacionais de Alto Desempenho (WSCAD), SBC, Oct. 2020, pp. 191–202. doi: 10.5753/wscad.2020.14069.
[26] F. P. G. De Sa, D. N. Brandao, E. Ogasawara, R. D. C. Coutinho, and R. F. Toso, “Wind Turbine Fault Detection: A Semi-Supervised Learning Approach with Automatic Evolutionary Feature Selection,” International Conference on Systems, Signals, and Image Processing, vol. 2020-July. pp. 323–328, 2020. doi: 10.1109/IWSSIP48289.2020.9145244.
[27] A. Ronald, R. Salles, K. Belloze, D. Pastore, and E. Ogasawara, “Modelo Autorregressivo de Integração Adaptativa,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), SBC, Nov. 2019, pp. 175–180. doi: 10.5753/sbbd.2019.8819.
[28] D. Oliveira, C. M. Abreu, E. Ogasawara, E. Bezerra, and L. de Lima, “A Science Gateway to Support Research in Spectral Graph Theory,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), SBC, Nov. 2019, pp. 217–222. doi: 10.5753/sbbd.2019.8826.
[29] D. N. R. da Silva et al., “A conceptual vision toward the management of machine learning models,” CEUR Workshop Proceedings, vol. 2469. pp. 15–27, 2019.
[30] F. Carvalho, L. F. Dos Santos, H. Borges, E. Ogasawara, and G. P. Guedes, “Discovering patterns in sentimental analysis,” Proceedings of the 25th Brazillian Symposium on Multimedia and the Web, WebMedia 2019. pp. 329–332, 2019. doi: 10.1145/3323503.3361683.
[31] L. R. Baroni et al., “Análise Exploratória da Malária na Amazônia Brasileira por Meio da Plataforma de Ciência de Dados aplicada à Saúde,” in Anais do Brazilian e-Science Workshop (BreSci), SBC, Jun. 2019. doi: 10.5753/bresci.2019.10025.
[32] P. Valduriez et al., “Scientific data analysis using data-intensive scalable computing: The SciDISC project,” CEUR Workshop Proceedings, vol. 2170. pp. 1–8, 2018.
[33] F. Porto, A. Krone-Martins, J. N. Rittmeyer, P. Valduriez, E. Ogasawara, and D. Shasha, “Point pattern search in big data,” ACM International Conference Proceeding Series. 2018. doi: 10.1145/3221269.3221294.
[34] F. Porto, A. Khatibi, J. G. Rittmeyer, E. Ogasawara, P. Valduriez, and D. Shasha, “Constellation Queries over Big Data,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), 2018.
[35] L. Moreira, C. Dantas, L. Oliveira, J. Soares, and E. Ogasawara, “On Evaluating Data Preprocessing Methods for Machine Learning Models for Flight Delays,” Proceedings of the International Joint Conference on Neural Networks, vol. 2018-July. 2018. doi: 10.1109/IJCNN.2018.8489294.
[36] C. Monteiro, E. Ogasawara, L. Goncalves, and J. R. De Toledo Quadros, “Control and security system for classroom access based on facial recognition,” Proceedings – 2018 44th Latin American Computing Conference, CLEI 2018. pp. 654–661, 2018. doi: 10.1109/CLEI.2018.00084.
[37] J. Ferreira, J. Soares, F. Porto, E. Pacitti, R. Coutinho, and E. Ogasawara, “Rumo à Integração da Álgebra de Workflows ao Processamento de Consultas Relacionais,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), 2018.
[38] J. A. Ferreira, F. Porto, R. Coutinho, and E. Ogasawara, “Rumo à Otimização de Operadores sobre UDF no Spark,” in Anais do Brazilian e-Science Workshop (BreSci), SBC, Jul. 2018. doi: 10.5753/bresci.2018.3280.
[39] A. B. Cruz et al., “Detecção de anomalias frequentes no transporte rodoviário urbano,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), 2018.
[40] P. Chaves et al., “Orthographic educational game for Portuguese language countries,” CSEDU 2018 – Proceedings of the 10th International Conference on Computer Supported Education, vol. 2. pp. 432–440, 2018. doi: 10.5220/0006757504320440.
[41] L. Carvalho et al., “Evaluating the complementarity of communication tools for learning platforms,” CSEDU 2018 – Proceedings of the 10th International Conference on Computer Supported Education, vol. 2. pp. 142–153, 2018. doi: 10.5220/0006798701420153.
[42] R. Campisano et al., “Discovering tight space-time sequences,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11031 LNCS. pp. 247–257, 2018. doi: 10.1007/978-3-319-98539-8_19.
[43] R. Salles, P. Mattos, E. Bezerra, L. Lima, and E. Ogasawara, “Avaliação de Agregação Temporal na Previsão da Temperatura de Superfície do Mar do Oceano Atlântico,” in Anais do Concurso de Trabalhos de Iniciação Científica da SBC (CTIC-SBC), SBC, Jul. 2017.
[44] R. Salles, L. Assis, G. Guedes, E. Bezerra, F. Porto, and E. Ogasawara, “A framework for benchmarking machine learning methods using linear models for univariate time series prediction,” Proceedings of the International Joint Conference on Neural Networks, vol. 2017-May. pp. 2338–2345, 2017. doi: 10.1109/IJCNN.2017.7966139.
[45] F. Paschoal, N. F. F. Ebecken, G. V. S. Ribeiro, L. M. De Aragao Daquer, R. C. Mauro, and E. S. Ogasawara, “FitRank – Social app to combat physical inactivity study of the use of fitness social apps on Facebook’s users profiles; [FitRank – Aplicativo Social de Combate ao Sedentarismo Estudo do uso de Aplicativos Sociais de Fitness em perfis de usuários do Facebook],” Iberian Conference on Information Systems and Technologies, CISTI. 2017. doi: 10.23919/CISTI.2017.7975688.
[46] A. Khatibi, F. Porto, J. G. Rittmeyer, E. Ogasawara, P. Valduriez, and D. Shasha, “Pre-processing and indexing techniques for constellation queries in big data,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10440 LNCS. pp. 164–172, 2017. doi: 10.1007/978-3-319-64283-3_12.
[47] J. Ferreira, D. Gaspar, B. Monteiro, A. B. Cruz, F. Porto, and E. Ogasawara, “Uma Proposta de Implementação de Álgebra de Workflows em Apache Spark no Apoio a Processos de Análise de Dados,” in Anais do Brazilian e-Science Workshop (BreSci), SBC, Jul. 2017, pp. 45–52. doi: 10.5753/bresci.2017.9921.
[48] A. B. Cruz, J. Ferreira, B. Monteiro, R. Coutinho, F. Porto, and E. Ogasawara, “Detecção de anomalias no transporte rodoviário urbano,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), 2017, pp. 240–245.
[49] R. G. Rodrigues, G. P. Guedes, and E. Ogasawara, “Towards a model for personality-based agents for emotional responses,” WebMedia 2016 – Proceedings of the 22nd Brazilian Symposium on Multimedia and the Web. pp. 359–362, 2016. doi: 10.1145/2976796.2988186.
[50] F. Paschoal, N. F. F. Ebecken, G. V. S. Ribeiro, L. M. De Aragao Daquer, R. C. Mauro, and E. S. Ogasawara, “Healthy behavior with social apps: Proposal for evolution study of the use of fitness social apps on Facebook; [Comportamento Saudável com Aplicativos Sociais: Proposta de estudo de evolução do uso de aplicativos sociais de fitness no Facebook],” Iberian Conference on Information Systems and Technologies, CISTI, vol. 2016-July. 2016. doi: 10.1109/CISTI.2016.7521484.
[51] E. Machado et al., “Exploring machine learning methods for the Star/Galaxy Separation Problem,” Proceedings of the International Joint Conference on Neural Networks, vol. 2016-October. pp. 123–130, 2016. doi: 10.1109/IJCNN.2016.7727189.
[52] R. Campisano, F. Porto, E. Pacitti, F. Masseglia, and E. Ogasawara, “Spatial Sequential Pattern Mining for Seismic Data,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), Salvador, BA, Oct. 2016.
[53] R. Salles, E. Bezerra, J. Soares, and E. Ogasawara, “Evaluating Linear Models as a Baseline for Time Series Imputation,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), Petrópolis, RJ, Oct. 2015.
[54] P. Mattos, A. M. Iorgulescu, R. Salles, E. Bezerra, E. Ogasawara, and L. Lima, “Uso de Redes Neurais para Previsão da Temperatura da Superfície do Mar do Oceano Atlântico Tropical,” in Anais do Brazilian e-Science Workshop (BreSci), SBC, Aug. 2015, pp. 131–140. doi: 10.5753/bresci.2015.7214.
[55] E. Machado et al., “Um Processo Exploratório para Classificação de Estrelas e Galáxias,” in Anais do Brazilian e-Science Workshop (BreSci), SBC, Aug. 2015, pp. 81–90. doi: 10.5753/bresci.2015.7209.
[56] E. Honorato et al., “Explorando uma Aplicação m-learning para Ensino de Vetores na Física do Ensino Médio,” in Anais do Simpósio Brasileiro de Informática na Educação, Oct. 2015, p. 1. doi: 10.5753/cbie.sbie.2015.1.
[57] G. P. Guedes, E. Bezerra, E. Ogasawara, and G. Xexéo, “Exploring multiple clusterings in attributed graphs,” Proceedings of the ACM Symposium on Applied Computing, vol. 13-17-April-2015. pp. 915–918, 2015. doi: 10.1145/2695664.2696008.
[58] W. Gomes et al., “Provendo um Serviço Web para Interação e Coleta de Dados de Aplicativos Educacionais,” in Anais do Simpósio Brasileiro de Informática na Educação, Oct. 2015, p. 957. doi: 10.5753/cbie.sbie.2015.957.
[59] A. B. Cruz et al., “Amê: An environment to learn and analyze adversarial search algorithms using stochastic card games,” Proceedings of the ACM Symposium on Applied Computing, vol. 13-17-April-2015. pp. 208–213, 2015. doi: 10.1145/2695664.2695734.
[60] A. Silva et al., “RFIDBook: Uma Abordagem para Programas de Bonificação Baseada em Redes Sociais,” in Anais do Brazilian Workshop on Social Network Analysis and Mining (BraSNAM), SBC, Aug. 2014, pp. 81–92.
[61] J. T. Pintas, D. De Oliveira, K. A. C. S. Ocaña, E. Ogasawara, and M. Mattoso, “SciLightning: A cloud provenance-based event notification for parallel workflows,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8377 LNCS. pp. 352–365, 2014. doi: 10.1007/978-3-319-06859-6_31.
[62] L. Paschoal et al., “JOE: Jogo Ortográfico Educacional,” in Anais do Simpósio Brasileiro de Informática na Educação, 2014, p. 652. doi: 10.5753/cbie.sbie.2014.652.
[63] R. Machado et al., “Arquitetura de um Simulador em Larga em Escala de Ataques Distribuídos de Negação de Serviço,” in Anais do Seminário Integrado de Software e Hardware (SEMISH), SBC, Jul. 2014, pp. 72–83.
[64] V. Lourenço, U. Souza, and E. Ogasawara, “Utilização de Algoritmos Genéticos para a Elaboração do Quadro de Horários do Ensino Médio-Técnico Integrado do CEFET/RJ,” in Anais do Encontro Nacional de Computação dos Institutos Federais (ENCompIF), SBC, Jul. 2014, pp. 5–8.
[65] F. S. Júnior, K. Belloze, F. P. Jr, E. Bezerra, J. R. Quadros, and E. Ogasawara, “Uma Abordagem Simplificada para Experimentação de Artefatos em Trabalhos de Conclusão de Curso,” in Anais do Workshop sobre Educação em Computação (WEI), SBC, Jul. 2014, pp. 299–308.
[66] G. Alves, P. Warley, J. Quadros, L. Lignani, and E. Ogasawara, “ControlHarvest: Ensino de Ecologia por Meio de Gamificação do Controle Biológico,” in Anais do Simpósio Brasileiro de Informática na Educação, 2014, p. 342. doi: 10.5753/cbie.sbie.2014.342.
[67] V. Silva, J. Dias, D. de Oliveira, E. Ogasawara, and M. Mattoso, “Uma Arquitetura P2P de Distribuição de Atividades para Execução Paralela de Workflows Científicos,” in Anais do Brazilian e-Science Workshop (BreSci), SBC, Jul. 2013, pp. 1795–1802.
[68] I. D. Santos, J. Dias, D. D. Oliveira, E. Ogasawara, K. Ocaña, and M. Mattoso, “Runtime dynamic structural changes of scientific workflows in clouds,” Proceedings – 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing, UCC 2013. pp. 417–422, 2013. doi: 10.1109/UCC.2013.83.
[69] I. de A. dos Santos, J. Dias, D. de Oliveira, E. Ogasawara, and M. Mattoso, “DynAdapt: Alterações na Definição de Atividades de Workflows Científicos em Tempo de Execução,” in Anais do Brazilian e-Science Workshop (BreSci), SBC, Jul. 2013, pp. 1831–1838.
[70] M. Mattoso et al., “User-steering of HPC workflows: State-of-the-art and future directions,” Proceedings of the 2nd ACM SIGMOD Workshop on Scalable Workflow Execution Engines and Technologies, SWEET 2013. 2013. doi: 10.1145/2499896.2499900.
[71] F. Horta et al., “Provenance traces from Chiron parallel workflow engine,” ACM International Conference Proceeding Series. pp. 337–338, 2013. doi: 10.1145/2457317.2457379.
[72] J. Dias, E. Ogasawara, D. De Oliveira, F. Porto, P. Valduriez, and M. Mattoso, “Algebraic dataflows for big data analysis,” Proceedings – 2013 IEEE International Conference on Big Data, Big Data 2013. pp. 150–155, 2013. doi: 10.1109/BigData.2013.6691567.
[73] D. De Oliveira, V. Viana, E. Ogasawara, K. Ocaña, and M. Mattoso, “Dimensioning the virtual cluster for parallel scientific workflows in clouds,” ScienceCloud 2013 – Proceedings of the 4th ACM Workshop on Scientific Cloud Computing. pp. 5–12, 2013. doi: 10.1145/2465848.2465852.
[74] F. Costa et al., “Capturing and querying workflow runtime provenance with PROV: A practical approach,” ACM International Conference Proceeding Series. pp. 282–289, 2013. doi: 10.1145/2457317.2457365.
[75] K. A. C. S. Ocaña, D. De Oliveira, F. Horta, J. Dias, E. Ogasawara, and M. Mattoso, “Exploring molecular evolution reconstruction using a parallel cloud based scientific workflow,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7409 LNBI. pp. 179–191, 2012. doi: 10.1007/978-3-642-31927-3_16.
[76] K. A. C. S. Ocaña, D. De Oliveira, J. Dias, E. Ogasawara, and M. Mattoso, “Discovering drug targets for neglected diseases using a pharmacophylogenomic cloud workflow,” 2012 IEEE 8th International Conference on E-Science, e-Science 2012. 2012. doi: 10.1109/eScience.2012.6404431.
[77] F. Horta, J. Dias, K. A. C. S. Ocaña, D. De Oliveira, E. Ogasawara, and M. Mattoso, “Poster: Using provenance to visualize data from large-scale experiments,” Proceedings – 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, SCC 2012. pp. 1418–1419, 2012. doi: 10.1109/SC.Companion.2012.228.
[78] J. C. de A. Gonçalves, D. de Oliveira, K. A. Ocana, E. Ogasawara, J. Dias, and M. Mattoso, “Using Provenance Analyzers to Improve the Performance of Scientific Workflows in Cloud Environments,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), 2012.
[79] J. C. D. A. R. Gonçalves, D. De Oliveira, K. A. C. S. Ocaña, E. Ogasawara, and M. Mattoso, “Using domain-specific data to enhance scientific workflow steering queries,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7525 LNCS. pp. 152–167, 2012. doi: 10.1007/978-3-642-34222-6_12.
[80] F. Costa, D. D. Oliveira, K. Ocana, E. Ogasawara, J. Dias, and M. Mattoso, “Handling failures in parallel scientific workflows using clouds,” Proceedings – 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, SCC 2012. pp. 129–139, 2012. doi: 10.1109/SC.Companion.2012.28.
[81] F. Costa, D. De Oliveira, E. Ogasawara, A. A. B. Lima, and M. Mattoso, “Athena: Text mining based discovery of scientific workflows in disperse repositories,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6799 LNCS. pp. 104–121, 2012. doi: 10.1007/978-3-642-27392-6_8.
[82] F. Costa, D. De Oliveira, K. A. C. S. Ocaña, E. Ogasawara, and M. Mattoso, “Enabling re-executions of parallel scientific workflows using runtime provenance data,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7525 LNCS. pp. 229–232, 2012. doi: 10.1007/978-3-642-34222-6_22.
[83] F. Chirigati et al., “Evaluating parameter sweep workflows in high performance computing,” ACM International Conference Proceeding Series. 2012. doi: 10.1145/2443416.2443418.
[84] V. Viana, D. de Oliveira, E. Ogasawara, and M. Mattoso, “SciCumulus-ECM: Um Serviço de Custos para a Execução de Workflows Científicos em Nuvens Computacionais,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), 2011.
[85] V. Silva et al., “Uma avaliação da Distribuição de Atividades Estática e Dinâmica em Ambientes Paralelos usando o Hydra,” in Anais do Brazilian e-Science Workshop (BreSci), 2011, pp. 1–8.
[86] D. de Oliveira, E. Ogasawara, F. Baiao, and M. Mattoso, “Adding Ontologies to Scientific Workflow Composition,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), 2011.
[87] E. Ogasawara et al., “A P2P approach to many tasks computing for scientific workflows,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6449 LNCS. pp. 327–339, 2011. doi: 10.1007/978-3-642-19328-6_31.
[88] K. A. C. S. Ocaña, D. De Oliveira, J. Dias, E. Ogasawara, and M. Mattoso, “Optimizing phylogenetic analysis using SciHmm cloud-based scientific workflow,” Proceedings – 2011 7th IEEE International Conference on eScience, eScience 2011. pp. 62–69, 2011. doi: 10.1109/eScience.2011.17.
[89] K. A. C. S. Ocaña, D. De Oliveira, E. Ogasawara, A. M. R. Dávila, A. A. B. Lima, and M. Mattoso, “SciPhy: A cloud-based workflow for phylogenetic analysis of drug targets in protozoan genomes,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6832 LNBI. pp. 66–70, 2011. doi: 10.1007/978-3-642-22825-4_9.
[90] J. Dias, E. Ogasawara, D. De Oliveira, F. Porto, A. L. G. A. Coutinho, and M. Mattoso, “Supporting dynamic parameter sweep in adaptive and user-steered workflows,” WORKS’11 – Proceedings of the 6th Workshop on Workflows in Support of Large-Scale Science, Co-located with SC’11. pp. 31–36, 2011. doi: 10.1145/2110497.2110502.
[91] J. Dias, E. Ogasawara, D. De Oliveira, and M. Mattoso, “Poster: Scientific data parallelism using P2P techniques,” SC’11 – Proceedings of the 2011 High Performance Computing Networking, Storage and Analysis Companion, Co-located with SC’11. pp. 27–28, 2011. doi: 10.1145/2148600.2148615.
[92] D. De Oliveira, K. Ocaña, E. Ogasawara, J. Dias, F. Baião, and M. Mattoso, “A performance evaluation of X-ray crystallography scientific workflow using scicumulus,” Proceedings – 2011 IEEE 4th International Conference on Cloud Computing, CLOUD 2011. pp. 708–715, 2011. doi: 10.1109/CLOUD.2011.99.
[93] E. Bezerra, B. Firmino, R. Castaneda, J. Soares, E. Ogasawara, and R. Goldschmidt, “A subjectivity detection method for opinion mining based on lexical resources,” Proceedings of the IADIS International Conference WWW/Internet 2011, ICWI 2011. pp. 317–324, 2011.
[94] V. Silva et al., “SimiFlow: Uma Arquitetura para Agrupamento de Workflows por Similaridade,” in Anais do Brazilian e-Science Workshop (BreSci), 2010, pp. 1–8.
[95] E. Silva, E. Ogasawara, D. Oliveira, M. Benevides, and M. Mattoso, “Especificação Formal e Verificação de Workflows Científicos,” in Anais do Brazilian e-Science Workshop (BreSci), 2010.
[96] E. Ogasawara, L. C. Martinez, D. De Oliveira, G. Zimbrão, G. L. Pappa, and M. Mattoso, “Adaptive Normalization: A novel data normalization approach for non-stationary time series,” Proceedings of the International Joint Conference on Neural Networks. 2010. doi: 10.1109/IJCNN.2010.5596746.
[97] A. Marinho et al., “Integrating provenance data from distributed workflow systems with ProvManager,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6378 LNCS. pp. 286–288, 2010. doi: 10.1007/978-3-642-17819-1_35.
[98] J. Dias, E. Ogasawara, D. De Oliveira, E. Pacitti, and M. Mattoso, “Improving many-task computing in scientific workflows using P2P techniques,” 2010 3rd Workshop on Many-Task Computing on Grids and Supercomputers, MTAGS10. 2010. doi: 10.1109/mtags.2010.5699430.
[99] D. De Oliveira, E. Ogasawara, F. Seabra, V. Silva, L. Murta, and M. Mattoso, “GExpLine: A tool for supporting experiment composition,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6378 LNCS. pp. 251–259, 2010. doi: 10.1007/978-3-642-17819-1_28.
[100] D. De Oliveira, E. Ogasawara, F. Baião, and M. Mattoso, “SciCumulus: A lightweigh cloud middleware to explore many task computing paradigm in scientific workflows,” Proceedings – 2010 IEEE 3rd International Conference on Cloud Computing, CLOUD 2010. pp. 378–385, 2010. doi: 10.1109/CLOUD.2010.64.
[101] F. Coutinho et al., “Data parallelism in bioinformatics workflows using Hydra,” HPDC 2010 – Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing. pp. 507–515, 2010. doi: 10.1145/1851476.1851550.
[102] D. de Oliveira et al., “Uma Abordagem Semântica para Linhas de Experimentos Científicos Usando Ontologias,” in Anais do Brazilian e-Science Workshop (BreSci), 2009.
[103] E. Ogasawara, P. Rangel, C. Werner, M. Mattoso, and L. Murta, “Comparison and versioning of scientific workflows,” Proceedings of the 2009 ICSE Workshop on Comparison and Versioning of Software Models, CVSM 2009. pp. 25–30, 2009. doi: 10.1109/CVSM.2009.5071718.
[104] E. Ogasawara, C. Paulino, L. Murta, C. Werner, and M. Mattoso, “Experiment line: Software reuse in scientific workflows,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5566 LNCS. pp. 264–272, 2009. doi: 10.1007/978-3-642-02279-1_20.
[105] E. Ogasawara, L. Murta, G. Zimbrão, and M. Mattoso, “Neural networks cartridges for data mining on time series,” Proceedings of the International Joint Conference on Neural Networks. pp. 2302–2309, 2009. doi: 10.1109/IJCNN.2009.5178615.
[106] E. Ogasawara et al., “Exploring many task computing in scientific workflows,” Proceedings of the 2nd ACM Workshop on Many-Task Computing on Grids and Supercomputers 2009, MTAGS ’09. 2009. doi: 10.1145/1646468.1646470.
[107] M. Mattoso et al., “Desafios no apoio à composição de experimentos científicos em larga escala,” in Seminário Integrado de Software e Hardware, 2009, p. 36.
[108] B. Costa, E. Ogasawara, L. Murta, and M. Mattoso, “Uma Estratégia de Versionamento de Workflows Científicos em Granularidade Fina,” in Anais do Brazilian e-Science Workshop (BreSci), 2009, pp. 49–56.
[109] E. Ogasawara, L. Murta, C. Werner, and M. Mattoso, “Linhas de Experimento: Reutilização e Gerência de Configuração em Workflows Científicos,” in Anais do Brazilian e-Science Workshop (BreSci), 2008, pp. 31–40.
[110] E. Ogasawara and M. Mattoso, “Uma Avaliação Experimental sobre Técnicas de Indexação em Bancos de Dados Orientados a Objetos,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), 1999, pp. 1–13.
[2] A. Castro et al., “GSTSM Package: Finding Frequent Sequences in Constrained Space and Time,” in BDA 2023 : 39ème Conférence sur la Gestion de Données – Principes, Technologies et Applications, BDA, Sep. 2023, pp. 1–2.
[3] B. Capistrano et al., “Desafios na Predição do Consumo de Pesticidas em Escala Global Usando Aprendizado de Máquina,” in Anais do Brazilian e-Science Workshop (BreSci), SBC, Sep. 2023, pp. 33–38. doi: 10.5753/bresci.2023.233831.
[4] H. Borges, A. Castro, R. Coutinho, F. Porto, E. Pacitti, and E. Ogasawara, “STMotif Explorer: A Tool for Spatiotemporal Motif Analysis,” in Anais Estendidos do Simpósio Brasileiro de Banco de Dados (SBBD), SBC, Sep. 2023, pp. 114–119. doi: 10.5753/sbbd_estendido.2023.233371.
[5] R. Zorrilla, E. Ogasawara, P. Valduriez, and F. Porto, “A Data-Driven Model Selection Approach to Spatio-Temporal Prediction,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), SBC, Sep. 2022, pp. 1–12. doi: 10.5753/sbbd.2022.224638.
[6] C. Pacheco et al., “Exploring Data Preprocessing and Machine Learning Methods for Forecasting Worldwide Fertilizers Consumption,” Proceedings of the International Joint Conference on Neural Networks, vol. 2022-July. 2022. doi: 10.1109/IJCNN55064.2022.9892325.
[7] J. Lima et al., “Forward and Backward Inertial Anomaly Detector: A Novel Time Series Event Detection Method,” Proceedings of the International Joint Conference on Neural Networks, vol. 2022-July. pp. 1–8, 2022. doi: 10.1109/IJCNN55064.2022.9892088.
[8] J. Lima et al., “Towards a cloud-based framework for online and integrated event detection,” in Anais Estendidos do Simpósio Brasileiro de Banco de Dados (SBBD), SBC, Sep. 2022, pp. 199–202. doi: 10.5753/sbbd_estendido.2022.21865.
[9] L. Izaú et al., “Towards Robust Cluster-Based Hyperparameter Optimization,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), SBC, Sep. 2022, pp. 439–444. doi: 10.5753/sbbd.2022.224330.
[10] A. J. M. da Fonseca, F. Porto, M. Ferro, E. Ogasawara, and E. Bezerra, “Analysis of precipitation data in Rio de Janeiro city using Extreme Value Theory,” in Anais Estendidos do Simpósio Brasileiro de Banco de Dados (SBBD), SBC, Sep. 2022, pp. 193–198. doi: 10.5753/sbbd_estendido.2022.21864.
[11] M. Ferro, E. Bezerra, E. Ogasawara, N. Moraes, and F. Porto, “Towards a Definition for Extreme Weather Events in Rio de Janeiro City,” in Anais Estendidos do Simpósio Brasileiro de Banco de Dados (SBBD), SBC, Sep. 2022, pp. 181–186. doi: 10.5753/sbbd_estendido.2022.21862.
[12] R. B. de Castro, V. C. Monteiro, R. Coutinho, H. Borges, and E. Ogasawara, “Identification of the North Brazil Current through spatial motifs in fixed time slices,” in Anais do Brazilian e-Science Workshop (BreSci), SBC, Jul. 2022, pp. 17–24. doi: 10.5753/bresci.2022.222622.
[13] C. Teixeira, L. Giusti, J. Soares, J. dos Santos, G. Amorim, and E. Ogasawara, “Integrated Dataset of Brazilian Flights,” in Anais do Brazilian e-Science Workshop (BreSci), SBC, Jul. 2021, pp. 89–96. doi: 10.5753/bresci.2021.15793.
[14] G. Souto, B. Capistrano, M. Matias, J. Soares, E. Ogasawara, and L. Giusti, “Avaliação dos diferentes tipos de redes LSTM para predição de ações na bolsa de valores,” in Anais da Escola Regional de Informática do Rio de Janeiro (ERI-RJ), SBC, Nov. 2021, pp. 65–71. doi: 10.5753/eri-rj.2021.18776.
[15] R. P. Salles, E. Ogasawara, and P. González, “Benchmarking Nonstationary Time Series Prediction,” in Anais Estendidos do Simpósio Brasileiro de Banco de Dados (SBBD), SBC, Oct. 2021, pp. 177–182. doi: 10.5753/sbbd_estendido.2021.18182.
[16] R. Pereira et al., “DJEnsemble: A Cost-Based Selection and Allocation of a Disjoint Ensemble of Spatio-Temporal Models,” ACM International Conference Proceeding Series. Association for Computing Machinery, pp. 226–231, 2021. doi: 10.1145/3468791.3468806.
[17] M. Mello, V. Belloni, F. Vasconcellos, J. Soares, E. Ogasawara, and L. Giusti, “Funções Executivas e Idade Relativa como Preditores de Sucesso no Futebol,” in Anais da Escola Regional de Informática do Rio de Janeiro (ERI-RJ), SBC, Nov. 2021, pp. 111–118. doi: 10.5753/eri-rj.2021.18782.
[18] C. Gea, J. Lima, E. Bezerra, and E. Ogasawara, “Análise de métodos de tratamento de outliers para predição dos retornos de índices de ações negociados em bolsa,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), SBC, Oct. 2021, pp. 277–282. doi: 10.5753/sbbd.2021.17885.
[19] M. B. Ferreira, M. Amorim, E. Ogasawara, and R. Barbastefano, “A interdisciplinaridade no desempenho da nota de matemática: um olhar para evolução do processo de ensino por meio de modelos regressivos,” in Anais da Escola Regional de Informática do Rio de Janeiro (ERI-RJ), SBC, Nov. 2021, pp. 41–48. doi: 10.5753/eri-rj.2021.18773.
[20] J. H. L. Fabian, A. T. A. Gomes, and E. Ogasawara, “Estimating the Execution Time of the Coupled Stage in Multiscale Numerical Simulations,” Communications in Computer and Information Science, vol. 1327. pp. 86–100, 2021. doi: 10.1007/978-3-030-68035-0_7.
[21] A. Castro et al., “Generalização de Mineração de Sequências Restritas no Espaço e no Tempo,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), SBC, Oct. 2021, pp. 313–318. doi: 10.5753/sbbd.2021.17891.
[22] R. Campos, H. Benini, J. dos Santos, E. Ogasawara, and F. Marques, “Classificação da Avaliação de Imersão em Aplicações Multissensoriais,” in Anais da Escola Regional de Informática do Rio de Janeiro (ERI-RJ), SBC, Nov. 2021, pp. 127–130. doi: 10.5753/eri-rj.2021.18785.
[23] C. Barros, R. Salles, E. Ogasawara, G. Guizzardi, and F. Porto, “Requirements for an ontology of digital twins,” CEUR Workshop Proceedings, vol. 2941. 2021.
[24] R. Salles et al., “Harbinger: Um framework para integração e análise de métodos de detecção de eventos em séries temporais,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), SBC, Sep. 2020, pp. 73–84. doi: 10.5753/sbbd.2020.13626.
[25] J. Fabian, A. Gomes, and E. Ogasawara, “Estimating the execution time of fully-online multiscale numerical simulations,” in Anais do Simpósio em Sistemas Computacionais de Alto Desempenho (WSCAD), SBC, Oct. 2020, pp. 191–202. doi: 10.5753/wscad.2020.14069.
[26] F. P. G. De Sa, D. N. Brandao, E. Ogasawara, R. D. C. Coutinho, and R. F. Toso, “Wind Turbine Fault Detection: A Semi-Supervised Learning Approach with Automatic Evolutionary Feature Selection,” International Conference on Systems, Signals, and Image Processing, vol. 2020-July. pp. 323–328, 2020. doi: 10.1109/IWSSIP48289.2020.9145244.
[27] A. Ronald, R. Salles, K. Belloze, D. Pastore, and E. Ogasawara, “Modelo Autorregressivo de Integração Adaptativa,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), SBC, Nov. 2019, pp. 175–180. doi: 10.5753/sbbd.2019.8819.
[28] D. Oliveira, C. M. Abreu, E. Ogasawara, E. Bezerra, and L. de Lima, “A Science Gateway to Support Research in Spectral Graph Theory,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), SBC, Nov. 2019, pp. 217–222. doi: 10.5753/sbbd.2019.8826.
[29] D. N. R. da Silva et al., “A conceptual vision toward the management of machine learning models,” CEUR Workshop Proceedings, vol. 2469. pp. 15–27, 2019.
[30] F. Carvalho, L. F. Dos Santos, H. Borges, E. Ogasawara, and G. P. Guedes, “Discovering patterns in sentimental analysis,” Proceedings of the 25th Brazillian Symposium on Multimedia and the Web, WebMedia 2019. pp. 329–332, 2019. doi: 10.1145/3323503.3361683.
[31] L. R. Baroni et al., “Análise Exploratória da Malária na Amazônia Brasileira por Meio da Plataforma de Ciência de Dados aplicada à Saúde,” in Anais do Brazilian e-Science Workshop (BreSci), SBC, Jun. 2019. doi: 10.5753/bresci.2019.10025.
[32] P. Valduriez et al., “Scientific data analysis using data-intensive scalable computing: The SciDISC project,” CEUR Workshop Proceedings, vol. 2170. pp. 1–8, 2018.
[33] F. Porto, A. Krone-Martins, J. N. Rittmeyer, P. Valduriez, E. Ogasawara, and D. Shasha, “Point pattern search in big data,” ACM International Conference Proceeding Series. 2018. doi: 10.1145/3221269.3221294.
[34] F. Porto, A. Khatibi, J. G. Rittmeyer, E. Ogasawara, P. Valduriez, and D. Shasha, “Constellation Queries over Big Data,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), 2018.
[35] L. Moreira, C. Dantas, L. Oliveira, J. Soares, and E. Ogasawara, “On Evaluating Data Preprocessing Methods for Machine Learning Models for Flight Delays,” Proceedings of the International Joint Conference on Neural Networks, vol. 2018-July. 2018. doi: 10.1109/IJCNN.2018.8489294.
[36] C. Monteiro, E. Ogasawara, L. Goncalves, and J. R. De Toledo Quadros, “Control and security system for classroom access based on facial recognition,” Proceedings – 2018 44th Latin American Computing Conference, CLEI 2018. pp. 654–661, 2018. doi: 10.1109/CLEI.2018.00084.
[37] J. Ferreira, J. Soares, F. Porto, E. Pacitti, R. Coutinho, and E. Ogasawara, “Rumo à Integração da Álgebra de Workflows ao Processamento de Consultas Relacionais,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), 2018.
[38] J. A. Ferreira, F. Porto, R. Coutinho, and E. Ogasawara, “Rumo à Otimização de Operadores sobre UDF no Spark,” in Anais do Brazilian e-Science Workshop (BreSci), SBC, Jul. 2018. doi: 10.5753/bresci.2018.3280.
[39] A. B. Cruz et al., “Detecção de anomalias frequentes no transporte rodoviário urbano,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), 2018.
[40] P. Chaves et al., “Orthographic educational game for Portuguese language countries,” CSEDU 2018 – Proceedings of the 10th International Conference on Computer Supported Education, vol. 2. pp. 432–440, 2018. doi: 10.5220/0006757504320440.
[41] L. Carvalho et al., “Evaluating the complementarity of communication tools for learning platforms,” CSEDU 2018 – Proceedings of the 10th International Conference on Computer Supported Education, vol. 2. pp. 142–153, 2018. doi: 10.5220/0006798701420153.
[42] R. Campisano et al., “Discovering tight space-time sequences,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11031 LNCS. pp. 247–257, 2018. doi: 10.1007/978-3-319-98539-8_19.
[43] R. Salles, P. Mattos, E. Bezerra, L. Lima, and E. Ogasawara, “Avaliação de Agregação Temporal na Previsão da Temperatura de Superfície do Mar do Oceano Atlântico,” in Anais do Concurso de Trabalhos de Iniciação Científica da SBC (CTIC-SBC), SBC, Jul. 2017.
[44] R. Salles, L. Assis, G. Guedes, E. Bezerra, F. Porto, and E. Ogasawara, “A framework for benchmarking machine learning methods using linear models for univariate time series prediction,” Proceedings of the International Joint Conference on Neural Networks, vol. 2017-May. pp. 2338–2345, 2017. doi: 10.1109/IJCNN.2017.7966139.
[45] F. Paschoal, N. F. F. Ebecken, G. V. S. Ribeiro, L. M. De Aragao Daquer, R. C. Mauro, and E. S. Ogasawara, “FitRank – Social app to combat physical inactivity study of the use of fitness social apps on Facebook’s users profiles; [FitRank – Aplicativo Social de Combate ao Sedentarismo Estudo do uso de Aplicativos Sociais de Fitness em perfis de usuários do Facebook],” Iberian Conference on Information Systems and Technologies, CISTI. 2017. doi: 10.23919/CISTI.2017.7975688.
[46] A. Khatibi, F. Porto, J. G. Rittmeyer, E. Ogasawara, P. Valduriez, and D. Shasha, “Pre-processing and indexing techniques for constellation queries in big data,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10440 LNCS. pp. 164–172, 2017. doi: 10.1007/978-3-319-64283-3_12.
[47] J. Ferreira, D. Gaspar, B. Monteiro, A. B. Cruz, F. Porto, and E. Ogasawara, “Uma Proposta de Implementação de Álgebra de Workflows em Apache Spark no Apoio a Processos de Análise de Dados,” in Anais do Brazilian e-Science Workshop (BreSci), SBC, Jul. 2017, pp. 45–52. doi: 10.5753/bresci.2017.9921.
[48] A. B. Cruz, J. Ferreira, B. Monteiro, R. Coutinho, F. Porto, and E. Ogasawara, “Detecção de anomalias no transporte rodoviário urbano,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), 2017, pp. 240–245.
[49] R. G. Rodrigues, G. P. Guedes, and E. Ogasawara, “Towards a model for personality-based agents for emotional responses,” WebMedia 2016 – Proceedings of the 22nd Brazilian Symposium on Multimedia and the Web. pp. 359–362, 2016. doi: 10.1145/2976796.2988186.
[50] F. Paschoal, N. F. F. Ebecken, G. V. S. Ribeiro, L. M. De Aragao Daquer, R. C. Mauro, and E. S. Ogasawara, “Healthy behavior with social apps: Proposal for evolution study of the use of fitness social apps on Facebook; [Comportamento Saudável com Aplicativos Sociais: Proposta de estudo de evolução do uso de aplicativos sociais de fitness no Facebook],” Iberian Conference on Information Systems and Technologies, CISTI, vol. 2016-July. 2016. doi: 10.1109/CISTI.2016.7521484.
[51] E. Machado et al., “Exploring machine learning methods for the Star/Galaxy Separation Problem,” Proceedings of the International Joint Conference on Neural Networks, vol. 2016-October. pp. 123–130, 2016. doi: 10.1109/IJCNN.2016.7727189.
[52] R. Campisano, F. Porto, E. Pacitti, F. Masseglia, and E. Ogasawara, “Spatial Sequential Pattern Mining for Seismic Data,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), Salvador, BA, Oct. 2016.
[53] R. Salles, E. Bezerra, J. Soares, and E. Ogasawara, “Evaluating Linear Models as a Baseline for Time Series Imputation,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), Petrópolis, RJ, Oct. 2015.
[54] P. Mattos, A. M. Iorgulescu, R. Salles, E. Bezerra, E. Ogasawara, and L. Lima, “Uso de Redes Neurais para Previsão da Temperatura da Superfície do Mar do Oceano Atlântico Tropical,” in Anais do Brazilian e-Science Workshop (BreSci), SBC, Aug. 2015, pp. 131–140. doi: 10.5753/bresci.2015.7214.
[55] E. Machado et al., “Um Processo Exploratório para Classificação de Estrelas e Galáxias,” in Anais do Brazilian e-Science Workshop (BreSci), SBC, Aug. 2015, pp. 81–90. doi: 10.5753/bresci.2015.7209.
[56] E. Honorato et al., “Explorando uma Aplicação m-learning para Ensino de Vetores na Física do Ensino Médio,” in Anais do Simpósio Brasileiro de Informática na Educação, Oct. 2015, p. 1. doi: 10.5753/cbie.sbie.2015.1.
[57] G. P. Guedes, E. Bezerra, E. Ogasawara, and G. Xexéo, “Exploring multiple clusterings in attributed graphs,” Proceedings of the ACM Symposium on Applied Computing, vol. 13-17-April-2015. pp. 915–918, 2015. doi: 10.1145/2695664.2696008.
[58] W. Gomes et al., “Provendo um Serviço Web para Interação e Coleta de Dados de Aplicativos Educacionais,” in Anais do Simpósio Brasileiro de Informática na Educação, Oct. 2015, p. 957. doi: 10.5753/cbie.sbie.2015.957.
[59] A. B. Cruz et al., “Amê: An environment to learn and analyze adversarial search algorithms using stochastic card games,” Proceedings of the ACM Symposium on Applied Computing, vol. 13-17-April-2015. pp. 208–213, 2015. doi: 10.1145/2695664.2695734.
[60] A. Silva et al., “RFIDBook: Uma Abordagem para Programas de Bonificação Baseada em Redes Sociais,” in Anais do Brazilian Workshop on Social Network Analysis and Mining (BraSNAM), SBC, Aug. 2014, pp. 81–92.
[61] J. T. Pintas, D. De Oliveira, K. A. C. S. Ocaña, E. Ogasawara, and M. Mattoso, “SciLightning: A cloud provenance-based event notification for parallel workflows,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8377 LNCS. pp. 352–365, 2014. doi: 10.1007/978-3-319-06859-6_31.
[62] L. Paschoal et al., “JOE: Jogo Ortográfico Educacional,” in Anais do Simpósio Brasileiro de Informática na Educação, 2014, p. 652. doi: 10.5753/cbie.sbie.2014.652.
[63] R. Machado et al., “Arquitetura de um Simulador em Larga em Escala de Ataques Distribuídos de Negação de Serviço,” in Anais do Seminário Integrado de Software e Hardware (SEMISH), SBC, Jul. 2014, pp. 72–83.
[64] V. Lourenço, U. Souza, and E. Ogasawara, “Utilização de Algoritmos Genéticos para a Elaboração do Quadro de Horários do Ensino Médio-Técnico Integrado do CEFET/RJ,” in Anais do Encontro Nacional de Computação dos Institutos Federais (ENCompIF), SBC, Jul. 2014, pp. 5–8.
[65] F. S. Júnior, K. Belloze, F. P. Jr, E. Bezerra, J. R. Quadros, and E. Ogasawara, “Uma Abordagem Simplificada para Experimentação de Artefatos em Trabalhos de Conclusão de Curso,” in Anais do Workshop sobre Educação em Computação (WEI), SBC, Jul. 2014, pp. 299–308.
[66] G. Alves, P. Warley, J. Quadros, L. Lignani, and E. Ogasawara, “ControlHarvest: Ensino de Ecologia por Meio de Gamificação do Controle Biológico,” in Anais do Simpósio Brasileiro de Informática na Educação, 2014, p. 342. doi: 10.5753/cbie.sbie.2014.342.
[67] V. Silva, J. Dias, D. de Oliveira, E. Ogasawara, and M. Mattoso, “Uma Arquitetura P2P de Distribuição de Atividades para Execução Paralela de Workflows Científicos,” in Anais do Brazilian e-Science Workshop (BreSci), SBC, Jul. 2013, pp. 1795–1802.
[68] I. D. Santos, J. Dias, D. D. Oliveira, E. Ogasawara, K. Ocaña, and M. Mattoso, “Runtime dynamic structural changes of scientific workflows in clouds,” Proceedings – 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing, UCC 2013. pp. 417–422, 2013. doi: 10.1109/UCC.2013.83.
[69] I. de A. dos Santos, J. Dias, D. de Oliveira, E. Ogasawara, and M. Mattoso, “DynAdapt: Alterações na Definição de Atividades de Workflows Científicos em Tempo de Execução,” in Anais do Brazilian e-Science Workshop (BreSci), SBC, Jul. 2013, pp. 1831–1838.
[70] M. Mattoso et al., “User-steering of HPC workflows: State-of-the-art and future directions,” Proceedings of the 2nd ACM SIGMOD Workshop on Scalable Workflow Execution Engines and Technologies, SWEET 2013. 2013. doi: 10.1145/2499896.2499900.
[71] F. Horta et al., “Provenance traces from Chiron parallel workflow engine,” ACM International Conference Proceeding Series. pp. 337–338, 2013. doi: 10.1145/2457317.2457379.
[72] J. Dias, E. Ogasawara, D. De Oliveira, F. Porto, P. Valduriez, and M. Mattoso, “Algebraic dataflows for big data analysis,” Proceedings – 2013 IEEE International Conference on Big Data, Big Data 2013. pp. 150–155, 2013. doi: 10.1109/BigData.2013.6691567.
[73] D. De Oliveira, V. Viana, E. Ogasawara, K. Ocaña, and M. Mattoso, “Dimensioning the virtual cluster for parallel scientific workflows in clouds,” ScienceCloud 2013 – Proceedings of the 4th ACM Workshop on Scientific Cloud Computing. pp. 5–12, 2013. doi: 10.1145/2465848.2465852.
[74] F. Costa et al., “Capturing and querying workflow runtime provenance with PROV: A practical approach,” ACM International Conference Proceeding Series. pp. 282–289, 2013. doi: 10.1145/2457317.2457365.
[75] K. A. C. S. Ocaña, D. De Oliveira, F. Horta, J. Dias, E. Ogasawara, and M. Mattoso, “Exploring molecular evolution reconstruction using a parallel cloud based scientific workflow,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7409 LNBI. pp. 179–191, 2012. doi: 10.1007/978-3-642-31927-3_16.
[76] K. A. C. S. Ocaña, D. De Oliveira, J. Dias, E. Ogasawara, and M. Mattoso, “Discovering drug targets for neglected diseases using a pharmacophylogenomic cloud workflow,” 2012 IEEE 8th International Conference on E-Science, e-Science 2012. 2012. doi: 10.1109/eScience.2012.6404431.
[77] F. Horta, J. Dias, K. A. C. S. Ocaña, D. De Oliveira, E. Ogasawara, and M. Mattoso, “Poster: Using provenance to visualize data from large-scale experiments,” Proceedings – 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, SCC 2012. pp. 1418–1419, 2012. doi: 10.1109/SC.Companion.2012.228.
[78] J. C. de A. Gonçalves, D. de Oliveira, K. A. Ocana, E. Ogasawara, J. Dias, and M. Mattoso, “Using Provenance Analyzers to Improve the Performance of Scientific Workflows in Cloud Environments,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), 2012.
[79] J. C. D. A. R. Gonçalves, D. De Oliveira, K. A. C. S. Ocaña, E. Ogasawara, and M. Mattoso, “Using domain-specific data to enhance scientific workflow steering queries,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7525 LNCS. pp. 152–167, 2012. doi: 10.1007/978-3-642-34222-6_12.
[80] F. Costa, D. D. Oliveira, K. Ocana, E. Ogasawara, J. Dias, and M. Mattoso, “Handling failures in parallel scientific workflows using clouds,” Proceedings – 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, SCC 2012. pp. 129–139, 2012. doi: 10.1109/SC.Companion.2012.28.
[81] F. Costa, D. De Oliveira, E. Ogasawara, A. A. B. Lima, and M. Mattoso, “Athena: Text mining based discovery of scientific workflows in disperse repositories,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6799 LNCS. pp. 104–121, 2012. doi: 10.1007/978-3-642-27392-6_8.
[82] F. Costa, D. De Oliveira, K. A. C. S. Ocaña, E. Ogasawara, and M. Mattoso, “Enabling re-executions of parallel scientific workflows using runtime provenance data,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7525 LNCS. pp. 229–232, 2012. doi: 10.1007/978-3-642-34222-6_22.
[83] F. Chirigati et al., “Evaluating parameter sweep workflows in high performance computing,” ACM International Conference Proceeding Series. 2012. doi: 10.1145/2443416.2443418.
[84] V. Viana, D. de Oliveira, E. Ogasawara, and M. Mattoso, “SciCumulus-ECM: Um Serviço de Custos para a Execução de Workflows Científicos em Nuvens Computacionais,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), 2011.
[85] V. Silva et al., “Uma avaliação da Distribuição de Atividades Estática e Dinâmica em Ambientes Paralelos usando o Hydra,” in Anais do Brazilian e-Science Workshop (BreSci), 2011, pp. 1–8.
[86] D. de Oliveira, E. Ogasawara, F. Baiao, and M. Mattoso, “Adding Ontologies to Scientific Workflow Composition,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), 2011.
[87] E. Ogasawara et al., “A P2P approach to many tasks computing for scientific workflows,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6449 LNCS. pp. 327–339, 2011. doi: 10.1007/978-3-642-19328-6_31.
[88] K. A. C. S. Ocaña, D. De Oliveira, J. Dias, E. Ogasawara, and M. Mattoso, “Optimizing phylogenetic analysis using SciHmm cloud-based scientific workflow,” Proceedings – 2011 7th IEEE International Conference on eScience, eScience 2011. pp. 62–69, 2011. doi: 10.1109/eScience.2011.17.
[89] K. A. C. S. Ocaña, D. De Oliveira, E. Ogasawara, A. M. R. Dávila, A. A. B. Lima, and M. Mattoso, “SciPhy: A cloud-based workflow for phylogenetic analysis of drug targets in protozoan genomes,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6832 LNBI. pp. 66–70, 2011. doi: 10.1007/978-3-642-22825-4_9.
[90] J. Dias, E. Ogasawara, D. De Oliveira, F. Porto, A. L. G. A. Coutinho, and M. Mattoso, “Supporting dynamic parameter sweep in adaptive and user-steered workflows,” WORKS’11 – Proceedings of the 6th Workshop on Workflows in Support of Large-Scale Science, Co-located with SC’11. pp. 31–36, 2011. doi: 10.1145/2110497.2110502.
[91] J. Dias, E. Ogasawara, D. De Oliveira, and M. Mattoso, “Poster: Scientific data parallelism using P2P techniques,” SC’11 – Proceedings of the 2011 High Performance Computing Networking, Storage and Analysis Companion, Co-located with SC’11. pp. 27–28, 2011. doi: 10.1145/2148600.2148615.
[92] D. De Oliveira, K. Ocaña, E. Ogasawara, J. Dias, F. Baião, and M. Mattoso, “A performance evaluation of X-ray crystallography scientific workflow using scicumulus,” Proceedings – 2011 IEEE 4th International Conference on Cloud Computing, CLOUD 2011. pp. 708–715, 2011. doi: 10.1109/CLOUD.2011.99.
[93] E. Bezerra, B. Firmino, R. Castaneda, J. Soares, E. Ogasawara, and R. Goldschmidt, “A subjectivity detection method for opinion mining based on lexical resources,” Proceedings of the IADIS International Conference WWW/Internet 2011, ICWI 2011. pp. 317–324, 2011.
[94] V. Silva et al., “SimiFlow: Uma Arquitetura para Agrupamento de Workflows por Similaridade,” in Anais do Brazilian e-Science Workshop (BreSci), 2010, pp. 1–8.
[95] E. Silva, E. Ogasawara, D. Oliveira, M. Benevides, and M. Mattoso, “Especificação Formal e Verificação de Workflows Científicos,” in Anais do Brazilian e-Science Workshop (BreSci), 2010.
[96] E. Ogasawara, L. C. Martinez, D. De Oliveira, G. Zimbrão, G. L. Pappa, and M. Mattoso, “Adaptive Normalization: A novel data normalization approach for non-stationary time series,” Proceedings of the International Joint Conference on Neural Networks. 2010. doi: 10.1109/IJCNN.2010.5596746.
[97] A. Marinho et al., “Integrating provenance data from distributed workflow systems with ProvManager,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6378 LNCS. pp. 286–288, 2010. doi: 10.1007/978-3-642-17819-1_35.
[98] J. Dias, E. Ogasawara, D. De Oliveira, E. Pacitti, and M. Mattoso, “Improving many-task computing in scientific workflows using P2P techniques,” 2010 3rd Workshop on Many-Task Computing on Grids and Supercomputers, MTAGS10. 2010. doi: 10.1109/mtags.2010.5699430.
[99] D. De Oliveira, E. Ogasawara, F. Seabra, V. Silva, L. Murta, and M. Mattoso, “GExpLine: A tool for supporting experiment composition,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6378 LNCS. pp. 251–259, 2010. doi: 10.1007/978-3-642-17819-1_28.
[100] D. De Oliveira, E. Ogasawara, F. Baião, and M. Mattoso, “SciCumulus: A lightweigh cloud middleware to explore many task computing paradigm in scientific workflows,” Proceedings – 2010 IEEE 3rd International Conference on Cloud Computing, CLOUD 2010. pp. 378–385, 2010. doi: 10.1109/CLOUD.2010.64.
[101] F. Coutinho et al., “Data parallelism in bioinformatics workflows using Hydra,” HPDC 2010 – Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing. pp. 507–515, 2010. doi: 10.1145/1851476.1851550.
[102] D. de Oliveira et al., “Uma Abordagem Semântica para Linhas de Experimentos Científicos Usando Ontologias,” in Anais do Brazilian e-Science Workshop (BreSci), 2009.
[103] E. Ogasawara, P. Rangel, C. Werner, M. Mattoso, and L. Murta, “Comparison and versioning of scientific workflows,” Proceedings of the 2009 ICSE Workshop on Comparison and Versioning of Software Models, CVSM 2009. pp. 25–30, 2009. doi: 10.1109/CVSM.2009.5071718.
[104] E. Ogasawara, C. Paulino, L. Murta, C. Werner, and M. Mattoso, “Experiment line: Software reuse in scientific workflows,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5566 LNCS. pp. 264–272, 2009. doi: 10.1007/978-3-642-02279-1_20.
[105] E. Ogasawara, L. Murta, G. Zimbrão, and M. Mattoso, “Neural networks cartridges for data mining on time series,” Proceedings of the International Joint Conference on Neural Networks. pp. 2302–2309, 2009. doi: 10.1109/IJCNN.2009.5178615.
[106] E. Ogasawara et al., “Exploring many task computing in scientific workflows,” Proceedings of the 2nd ACM Workshop on Many-Task Computing on Grids and Supercomputers 2009, MTAGS ’09. 2009. doi: 10.1145/1646468.1646470.
[107] M. Mattoso et al., “Desafios no apoio à composição de experimentos científicos em larga escala,” in Seminário Integrado de Software e Hardware, 2009, p. 36.
[108] B. Costa, E. Ogasawara, L. Murta, and M. Mattoso, “Uma Estratégia de Versionamento de Workflows Científicos em Granularidade Fina,” in Anais do Brazilian e-Science Workshop (BreSci), 2009, pp. 49–56.
[109] E. Ogasawara, L. Murta, C. Werner, and M. Mattoso, “Linhas de Experimento: Reutilização e Gerência de Configuração em Workflows Científicos,” in Anais do Brazilian e-Science Workshop (BreSci), 2008, pp. 31–40.
[110] E. Ogasawara and M. Mattoso, “Uma Avaliação Experimental sobre Técnicas de Indexação em Bancos de Dados Orientados a Objetos,” in Anais do Simpósio Brasileiro de Banco de Dados (SBBD), 1999, pp. 1–13.