Periódicos
[1] R. Salles, B. Lange, R. Akbarinia, F. Masseglia, E. Ogasawara, and E. Pacitti, “Scalable and accurate online multivariate anomaly detection,” Information Systems, vol. 131, p. 102524, Jun. 2025, doi: 10.1016/j.is.2025.102524.
[2] R. Salles, J. Lima, M. Reis, R. Coutinho, E. Pacitti, F. Masseglia, R. Akbarinia, C. Chen, J. Garibaldi, F. Porto, and E. Ogasawara, “SoftED: Metrics for soft evaluation of time series event detection,” Computers and Industrial Engineering, vol. 198. 2024. doi: 10.1016/j.cie.2024.110728.
[3] F. Marques, L. Lignani, J. Quadros, M. Amorim, W. Viana, E. Ogasawara, and J. dos Santos, “ProBee: A Provenance-based Design for an Educational Game Analytics Model,” Technology, Knowledge and Learning. 2024. doi: 10.1007/s10758-024-09758-x.
[4] D. S. de Salles, C. Gea, C. E. Mello, L. Assis, R. Coutinho, E. Bezerra, and E. Ogasawara, “Multi-Scale Event Detection in Financial Time Series,” Computational Economics. 2024. doi: 10.1007/s10614-024-10582-9.
[5] 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, vol. 58, no. 1. pp. 203–222, 2024. doi: 10.1007/s10579-023-09647-2.
[6] J. Souza, C. Boccolini, L. Baroni, K. Belloze, E. Bezerra, M. Pedroso, R. F. S. Alves, and E. Ogasawara, “Evaluation of statistical process control charts for infant mortality monitoring in Brazilian cities with different population sizes,” BMC Research Notes, vol. 17, no. 1. 2024. doi: 10.1186/s13104-024-06943-0.
[7] A. Mello, L. Giusti, T. Tavares, F. Alexandrino, G. Guedes, J. Soares, R. Barbastefano, F. Porto, D. Carvalho, and E. Ogasawara, “D-AI2-M: Ethanol Production Forecasting in Brazil Using Data-Centric Artificial Intelligence Methodology,” IEEE Latin America Transactions, vol. 22, no. 11, Art. no. 11, Oct. 2024.
[8] L. Baroni, L. Scoralick, A. Reis, K. Belloze, M. Pedroso, R. Alves, C. Boccolini, P. Boccolini, and E. Ogasawara, “A contextual-compositional approach to discover associations between health determinants and health indicators for neonatal mortality rate monitoring in situations of anomalies,” PLOS ONE, vol. 19, no. 12, p. e0310413, de dez. de 2024, doi: 10.1371/journal.pone.0310413.
[9] 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.
[10] P. Elias, H. de S. Campos, 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.
[11] C. Gea, L. Vereda, and E. Ogasawara, “Detection of Uncertainty Events in the Brazilian Economic and Financial Time Series,” Computational Economics. 2023. doi: 10.1007/s10614-023-10483-3.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] L. Giusti, L. Carvalho, A. T. Gomes, R. Coutinho, J. Soares, and E. Ogasawara, “Analyzing flight delay prediction under concept drift,” Evolving Systems, vol. 13, no. 5. pp. 723–736, 2022. doi: 10.1007/s12530-021-09415-z.
[17] C. Teixeira, L. Fragoso, M. Mattoso, D. Carvalho, E. Bezerra, J. Soares, G. Amorim, and E. Ogasawara, “A horizontal partitioning-based method for frequent pattern mining in transport timetable,” Expert Systems, vol. 39, no. 2. 2022. doi: 10.1111/exsy.12881.
[18] 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.
[19] L. Carvalho, A. Sternberg, L. Maia Gonçalves, A. Beatriz Cruz, J. A. Soares, D. Brandão, D. Carvalho, and E. Ogasawara, “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.
[20] 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.
[21] L. Escobar, R. Salles, J. Lima, C. Gea, L. Baroni, A. Ziviani, P. Pires, F. Delicato, R. Coutinho, L. Assis, and E. Ogasawara, “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.
[22] B. Paixão, L. Baroni, M. Pedroso, R. Salles, L. Escobar, C. de Sousa, R. de Freitas Saldanha, J. Soares, R. Coutinho, F. Porto, and E. Ogasawara, “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.
[23] J. Cardoso, D. Caetano, R. Abreu, J. Quadros, J. D. Santos, E. Ogasawara, and L. Lignani, “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.
[24] H. Borges, M. Dutra, A. Bazaz, R. Coutinho, F. Perosi, F. Porto, F. Masseglia, E. Pacitti, and E. Ogasawara, “Spatial-time motifs discovery,” Intelligent Data Analysis, vol. 24, no. 5. pp. 1121–1140, 2020. doi: 10.3233/IDA-194759.
[25] 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.
[26] 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.
[27] L. Baroni, M. Pedroso, C. Barcellos, R. Salles, S. Salles, B. Paixão, A. Chrispino, G. Guedes, and E. Ogasawara, “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.
[28] L. Baroni, R. Salles, S. Salles, G. Guedes, F. Porto, E. Bezerra, C. Barcellos, M. Pedroso, and E. Ogasawara, “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.
[29] 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.
[30] A. Marinho, D. de Oliveira, E. Ogasawara, V. Silva, K. Ocaña, L. Murta, V. Braganholo, and M. Mattoso, “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.
[31] 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.
[32] 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.
[33] 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.
[34] 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, Art. no. 3, Oct. 2015, doi: 10.5335/rbca.2015.4506.
[35] M. Mattoso, J. Dias, K. A. C. S. Ocaña, E. Ogasawara, F. Costa, F. Horta, V. Silva, and D. De Oliveira, “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.
[36] J. R. de T. Quadros, D. Oliveira, A. Queiroz, E. Ogasawara, and C. Schocair, “Towards a UML-based Reference Model for Blended Learning,” International Journal of Recent Contributions from Engineering, Science & IT (iJES), vol. 2, no. 3, Art. no. 3, Aug. 2014, doi: 10.3991/ijes.v2i3.3818.
[37] 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.
[38] 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.
[39] D. De Oliveira, K. A. C. S. Ocaña, E. Ogasawara, J. Dias, J. Gonçalves, F. Baião, and M. Mattoso, “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.
[40] 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.
[41] 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.
[42] J. R. de T. Quadros, R. Castaneda, M. Amorim, G. Herzog, L. Carneiro, K. Menezes, M. Pinheiro, D. de Oliveira, and E. Ogasawara, “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.
[43] E. Ogasawara, J. Dias, V. Silva, F. Chirigati, D. De Oliveira, F. Porto, P. Valduriez, and M. Mattoso, “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.
[44] E. Ogasawara, D. De Oliveira, F. Paschoal Jr., R. Castaneda, M. Amorim, R. Mauro, J. Soares, J. Quadros, and E. Bezerra, “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.
[45] G. Guerra, F. A. Rochinha, R. Elias, D. de Oliveira, E. Ogasawara, J. F. Dias, M. Mattoso, and A. L. G. A. Coutinho, “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.
[46] A. Marinho, L. Murta, C. Werner, V. Braganholo, S. M. S. D. Cruz, E. Ogasawara, and M. Mattoso, “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.
[47] 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.
[48] 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.
[49] M. C. Garbin, S. F. do Amaral, C. O. S. Mendes, E. Ogasawara, and J. M. de S. Rocha, “Adaptation of the Moodle for Application in Distance Education Course at the State University of Campinas,” Procedia – Social and Behavioral Sciences, vol. 46, pp. 2514–2518, Jan. 2012, doi: 10.1016/j.sbspro.2012.05.513.
[50] V. Silva, F. Chirigati, K. Maia, E. Ogasawara, D. Oliveira, V. Braganholo, and M. Mattoso, “Similarity-based workflow clustering,” Journal of Computational Interdisciplinary Sciences, vol. 2, no. 1, 2011.
[51] F. Coutinho, E. Ogasawara, D. De Oliveira, V. Braganholo, A. A. B. Lima, A. M. R. Dávila, and M. Mattoso, “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.
[52] 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.
[53] M. Mattoso, C. Werner, G. H. Travassos, V. Braganholo, E. Ogasawara, D. De Oliveira, S. M. S. Da Cruz, W. Martinho, and L. Murta, “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, J. Lima, M. Reis, R. Coutinho, E. Pacitti, F. Masseglia, R. Akbarinia, C. Chen, J. Garibaldi, F. Porto, and E. Ogasawara, “SoftED: Metrics for soft evaluation of time series event detection,” Computers and Industrial Engineering, vol. 198. 2024. doi: 10.1016/j.cie.2024.110728.
[3] F. Marques, L. Lignani, J. Quadros, M. Amorim, W. Viana, E. Ogasawara, and J. dos Santos, “ProBee: A Provenance-based Design for an Educational Game Analytics Model,” Technology, Knowledge and Learning. 2024. doi: 10.1007/s10758-024-09758-x.
[4] D. S. de Salles, C. Gea, C. E. Mello, L. Assis, R. Coutinho, E. Bezerra, and E. Ogasawara, “Multi-Scale Event Detection in Financial Time Series,” Computational Economics. 2024. doi: 10.1007/s10614-024-10582-9.
[5] 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, vol. 58, no. 1. pp. 203–222, 2024. doi: 10.1007/s10579-023-09647-2.
[6] J. Souza, C. Boccolini, L. Baroni, K. Belloze, E. Bezerra, M. Pedroso, R. F. S. Alves, and E. Ogasawara, “Evaluation of statistical process control charts for infant mortality monitoring in Brazilian cities with different population sizes,” BMC Research Notes, vol. 17, no. 1. 2024. doi: 10.1186/s13104-024-06943-0.
[7] A. Mello, L. Giusti, T. Tavares, F. Alexandrino, G. Guedes, J. Soares, R. Barbastefano, F. Porto, D. Carvalho, and E. Ogasawara, “D-AI2-M: Ethanol Production Forecasting in Brazil Using Data-Centric Artificial Intelligence Methodology,” IEEE Latin America Transactions, vol. 22, no. 11, Art. no. 11, Oct. 2024.
[8] L. Baroni, L. Scoralick, A. Reis, K. Belloze, M. Pedroso, R. Alves, C. Boccolini, P. Boccolini, and E. Ogasawara, “A contextual-compositional approach to discover associations between health determinants and health indicators for neonatal mortality rate monitoring in situations of anomalies,” PLOS ONE, vol. 19, no. 12, p. e0310413, de dez. de 2024, doi: 10.1371/journal.pone.0310413.
[9] 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.
[10] P. Elias, H. de S. Campos, 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.
[11] C. Gea, L. Vereda, and E. Ogasawara, “Detection of Uncertainty Events in the Brazilian Economic and Financial Time Series,” Computational Economics. 2023. doi: 10.1007/s10614-023-10483-3.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] L. Giusti, L. Carvalho, A. T. Gomes, R. Coutinho, J. Soares, and E. Ogasawara, “Analyzing flight delay prediction under concept drift,” Evolving Systems, vol. 13, no. 5. pp. 723–736, 2022. doi: 10.1007/s12530-021-09415-z.
[17] C. Teixeira, L. Fragoso, M. Mattoso, D. Carvalho, E. Bezerra, J. Soares, G. Amorim, and E. Ogasawara, “A horizontal partitioning-based method for frequent pattern mining in transport timetable,” Expert Systems, vol. 39, no. 2. 2022. doi: 10.1111/exsy.12881.
[18] 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.
[19] L. Carvalho, A. Sternberg, L. Maia Gonçalves, A. Beatriz Cruz, J. A. Soares, D. Brandão, D. Carvalho, and E. Ogasawara, “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.
[20] 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.
[21] L. Escobar, R. Salles, J. Lima, C. Gea, L. Baroni, A. Ziviani, P. Pires, F. Delicato, R. Coutinho, L. Assis, and E. Ogasawara, “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.
[22] B. Paixão, L. Baroni, M. Pedroso, R. Salles, L. Escobar, C. de Sousa, R. de Freitas Saldanha, J. Soares, R. Coutinho, F. Porto, and E. Ogasawara, “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.
[23] J. Cardoso, D. Caetano, R. Abreu, J. Quadros, J. D. Santos, E. Ogasawara, and L. Lignani, “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.
[24] H. Borges, M. Dutra, A. Bazaz, R. Coutinho, F. Perosi, F. Porto, F. Masseglia, E. Pacitti, and E. Ogasawara, “Spatial-time motifs discovery,” Intelligent Data Analysis, vol. 24, no. 5. pp. 1121–1140, 2020. doi: 10.3233/IDA-194759.
[25] 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.
[26] 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.
[27] L. Baroni, M. Pedroso, C. Barcellos, R. Salles, S. Salles, B. Paixão, A. Chrispino, G. Guedes, and E. Ogasawara, “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.
[28] L. Baroni, R. Salles, S. Salles, G. Guedes, F. Porto, E. Bezerra, C. Barcellos, M. Pedroso, and E. Ogasawara, “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.
[29] 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.
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[110] 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.
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[77] 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.
[78] A. B. Cruz, S. Serique, L. Preuss, A. Ogasawara, J. Quadros, E. Bezerra, U. Souza, and E. Ogasawara, “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.
[79] 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.
[80] 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.
[81] 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.
[82] A. Silva, F. Rosa, I. Rocha, F. P. P. Junior, E. Bezerra, G. Guedes, and E. Ogasawara, “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.
[83] L. Paschoal, T. Bento, T. Velasco, C. O. Schocair, R. Castaneda, T. Oliveira, and E. Ogasawara, “JOE: Jogo Ortográfico Educacional,” in Anais do Simpósio Brasileiro de Informática na Educação, 2014, p. 652. doi: 10.5753/cbie.sbie.2014.652.
[84] 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.
[85] R. Machado, M. Santos, H. Soares, E. Ogasawara, F. David, R. Soares, and B. Guimarães, “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.
[86] M. Mattoso, K. Ocãa, F. Horta, J. Dias, E. Ogasawara, V. Silva, D. De Oliveira, F. Costa, and I. Araújo, “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.
[87] 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.
[88] 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.
[89] F. Horta, V. Silva, F. Costa, D. De Oliveira, K. Ocaña, E. Ogasawara, J. Dias, and M. Mattoso, “Provenance traces from Chiron parallel workflow engine,” ACM International Conference Proceeding Series. pp. 337–338, 2013. doi: 10.1145/2457317.2457379.
[90] 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.
[91] 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.
[92] F. Costa, V. Silva, D. De Oliveira, K. Ocaña, E. Ogasawara, J. Dias, and M. Mattoso, “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.
[93] 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.
[94] 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.
[95] 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.
[96] 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.
[97] 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.
[98] F. Chirigati, V. Silva, E. Ogasawara, D. De Oliveira, J. Dias, F. Porto, P. Valduriez, and M. Mattoso, “Evaluating parameter sweep workflows in high performance computing,” ACM International Conference Proceeding Series. 2012. doi: 10.1145/2443416.2443418.
[99] 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.
[100] 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.
[101] 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.
[102] F. Horta, J. Dias, K. A. C. S. Ocaña, D. de Oliveira, E. Ogasawara, and M. Mattoso, “Abstract: Using Provenance to Visualize Data from Large-Scale Experiments,” in 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, Nov. 2012, pp. 1418–1419. doi: 10.1109/SC.Companion.2012.228.
[103] V. Silva, F. Chirigati, E. Ogasawara, J. Dias, D. Oliveira, F. Porto, P. Valduriez, and M. Mattoso, “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.
[104] 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.
[105] 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.
[106] 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.
[107] 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.
[108] 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.
[109] 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.
[110] 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.
[111] 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.
[112] E. Ogasawara, J. Dias, D. Oliveira, C. Rodrigues, C. Pivotto, R. Antas, V. Braganholo, P. Valduriez, and M. Mattoso, “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.
[113] V. Silva, F. Chirigati, K. Maia, E. Ogasawara, D. Oliveira, V. Braganholo, L. Murta, and M. Mattoso, “SimiFlow: Uma Arquitetura para Agrupamento de Workflows por Similaridade,” in Anais do Brazilian e-Science Workshop (BreSci), 2010, pp. 1–8.
[114] J. Dias, C. Rodrigues, E. Ogasawara, D. D. Oliveira, V. Braganholo, E. Pacitti, and M. Mattoso, “SciMulator: Um Ambiente de Simulação de Workflows Científicos em Redes P2P,” presented at the VI Workshop de Redes Dinâmicas e Sistemas Peer-to-Peer 2010, May 2010, p. 12.
[115] 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.
[116] A. Marinho, L. Murta, C. Werner, V. Braganholo, E. Ogasawara, S. M. S. Da Cruz, and M. Mattoso, “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.
[117] 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.
[118] 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.
[119] 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.
[120] F. Coutinho, E. Ogasawara, D. De Oliveira, V. Braganholo, A. A. B. Lima, A. M. R. Dávila, and M. Mattoso, “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.
[121] 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.
[122] 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.
[123] D. de Oliveira, E. Ogasawara, F. Chirigati, V. Sousa, L. Murta, C. Werner, and M. Mattoso, “Uma Abordagem Semântica para Linhas de Experimentos Científicos Usando Ontologias,” in Anais do Brazilian e-Science Workshop (BreSci), 2009.
[124] 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.
[125] E. Ogasawara, D. De Oliveira, F. Chirigati, C. E. Barbosa, R. Elias, V. Braganholo, A. Coutinho, and M. Mattoso, “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.
[126] 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.
[127] M. Mattoso, C. Werner, G. Travassos, V. Braganholo, L. Murta, E. Ogasawara, F. Oliveira, and W. Martinho, “Desafios no apoio à composição de experimentos científicos em larga escala,” in Seminário Integrado de Software e Hardware, 2009, p. 36.
[128] 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.
[129] 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.
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