Return to SciDISC (2017-2019) with LNCC, UFRJ, UFF, CEFET (Brazil)

SciDISC publications

2018

[Bazaz 2018] A Bazaz, H. Borges, E. Ogasawara, STMotif: Discovery of Motifs in Spatial-Time Series, CRAN Repository: https://cran.r-project.org/web/packages/STMotif/index.html, 2018.

[Camata 2018] J.  Camata, V. Sousa, P. Valduriez, M. Mattoso, A. Coutinho. In situ visualization and data analysis for turbidity currents simulation. Computers & Geosciences 110: 23-31, 2018.

[Campisano 2018] R. Campisano, H. Borges, F. Porto, F. Perosi, E. Pacitti, F. Masseglia, E. Ogasawara. Discovering Tight Space-Time Sequences. Int. Conf. on Big Data Analytics and Knowledge Discovery (DaWaK), 247-257, 2018

[Cruz 2018] A. Cruz, J. Ferreira, D. Carvalho, E. Mendes, E. Pacitti, R. Coutinho, F. Porto, E. Ogasawara. Detecçao de Anomalias Frequentes no Transporte Rodoviario Urbano. Brazilian Symposium on Databases (SBBD), 271-276, 2018.

[Ferreira 2018] J. Ferreira, J. Soares, F. Porto, E. Pacitti, R. Coutinho, E. Ogasawara. Rumo à Integração da Álgebra de Workflows com o Processamento de Consulta Relacional. Brazilian Symposium on Databases (SBBD), 205-210, 2018.

[Liu 2018a] J. Liu, N. Lemus, E. Pacitti, F. Porto, P. Valduriez. Computation of PDFs on Big Spatial Data: Problem & Architecture. Latin America Data Science Workshop (LADaS), in conjunction with VLDB2018, 6 pages, 2018.

[Liu 2018b] J. Liu, L. Pineda, E. Pacitti, A. Costan, P. Valduriez, G. Antoniu, M. Mattoso. Efficient Scheduling of Scientific Workflows using Hot Metadata in a Multisite Cloud. IEEE Trans. on Knowledge and Data Engineering (TKDE), In press, pp.1-20,  DOI: 10.1109/TKDE.2018.2867857, 2018.

[Porto 2018a] F. Porto, A. Khatibi, J. Rittmeyer, E. Ogasawara, P. Valduriez, D. Shasha. Constellation Queries over Big Data. Brazilian Symposium on Databases (SBBD), 85-96, 2018.

[Porto 2018b] F. Porto, J. Rittmeyer, E. Ogasawara, A. Krone-Martins, P. Valduriez, D. Shasha.  Point Pattern Search in Big Data. Int. Conf. on Scientific and Statistical Database Management (SSDBM),21:1-21:12, 2018.

[Silva 2018a] V. Silva. Analysis of raw data from multiple data sources during the execution of computational simulations. Ph.D. Thesis, UFRJ, 2018.

[Silva 2018b] V. Silva, D. de Oliveira, P. Valduriez, M. Mattoso. DfAnalyzer: Runtime Dataflow Analysis of Scientific Applications using Provenance. Proceedings of the VLDB Endowment (PVLDB), 11(12): 2082-2085, 2018.

[Silva 2018c] V. Silva, R. Souza, J. Camata, D. de Oliveira, P. Valduriez, A. Coutinho, M. Mattoso. Capturing Provenance for Runtime Data Analysis in Computational Science and Engineering Applications. International Provenance and Annotation Workshop (IPAW), LNCS v. 11017. p. 183-187, 2018.

[Silva 2018d] V. Silva, J. Camata, D. de Oliveira, P. Valduriez, M. Mattoso, A. Coutinho. Integrating In-Situ Data Analysis and Visualization on libMesh library. World Congress in Computational Mechanics, (WCCM), 2018.

[Silva Jr. 2018a] D. Silva Jr., A. Paes, E. Pacitti, D. de Oliveira. FReeP: towards parameter recommendation in scientific workflows using preference learning. Brazilian Symposium on Databases (SBBD), 211-216, 2018.

[Silva Jr. 2018b] D. Silva Jr., A. Paes, E. Pacitti, D. Oliveira. Data Quality Prediction in Scientific Workflows. In preparation, 2018.

[Souza 2018] R. Souza, V. Silva, A. Coutinho, P. Valduriez, M. Mattoso. Data Reduction in Scientific Workflows using Provenance Monitoring and User Steering. Future Generation Computer Systems (FGCS), Elsevier, In press, pp.1-21, DOI: 10.1016/j.future.2017.11.028, 2018.

[Valduriez 2018] P. Valduriez, M. Mattoso, R. Akbarinia, H. Borges, J. Camata, A. Coutinho, D. Gaspar, N. Lemus, J. Liu, H. Lustosa, F. Masseglia, F. Noriegua da Silva, V. Silva, R. Souza, K. Ocaña, E. Ogasawara, D. Oliveira, E. Pacitti, F. Porto, D. Shasha. Scientific Data Analysis Using Data-Intensive Scalable Computing: the SciDISC Project. Latin America Data Science Workshop (LADaS), in conjunction with VLDB2018, 8 pages, 2018.

2017

[Khatibi 2017] A. Khatibi, F. Porto, J. Rittmeyer, E. Ogasawara, P. Valduriez, D. Shasha. Pre-processing and Indexing Techniques for Constellation Queries in Big Data. Int. Conf. on Big Data Analytics and Knowledge Discovery (DaWaK), 164-172, 2017.

[Liu 2017] J. Liu, E. Pacitti, P. Valduriez, M. Mattoso. Scientific Workflow Scheduling with Provenance Data in a Multisite Cloud. Trans. on Large-Scale Data- and Knowledge-Centered Systems (TLDKS), 33: 80-112, 2017.

[Pineda-Morales 2017] L. Pineda-Morales, J. Liu, A. Costany, E Pacitti, G. Antoniu, P. Valduriez, M. Mattoso. Efficient Scheduling of Scientific Workflows using Hot Metadata in a Multisite Cloud. BDA : Conf. sur la Gestion de Données — Principes, Technologies et Applications, 2017.

[Camata 2017] J. Camata, V. Silva, P. Valduriez, M. Mattoso, A. Coutinho. In Situ Visualization and Data Analysis for Turbidity Currents Simulation. Computers & Geosciences, 110, pp.23 – 31, 2017.

[Campisano 2017], R. Campisano, Sequence Mining in Spatial-Time Series (Master Degree Dissertation), CEFET/RJ, 2017.

[Cruz 2017], A.B. Cruz, J. Ferreira, B. Monteiro, R. Coutinho, F. Porto, E. Ogasawara, Detecção de Anomalias no Transporte Rodoviário Urbano, Brazilian Symposium on Databases (SBBD), 2017.

[Hermano 2017] H. Lustosa, F. Porto, N. Lemus, P. Valduriez, TARS: Na Extension of the Multi-dimensional Array Model, ER FORUM – Conceptual Modeling : Research In Progress, Valencia, 2017

[Porto 2017] F. Porto, A. Khatibi, J. Nobre, E. Ogasawara, P. Valduriez, D. Shasha. Constellation Queries over Big Data. CoRR abs/1703.02638, 2017.

[Gaspar 2017] D. Gaspar, F. Porto, R. Akbarinia, E. Pacitti, TARDIS: Optimal Execution of Scientific Workflows in Apache Spark. Int. Conf. on Big Data Analytics and Knowledge Discovery (DaWaK), 74-87, 2017.

[Silva 2017] V. Silva, J. Leite, J. Camata, D. de Oliveira, A. Coutinho, P. Valduriez, M. Mattoso. Raw data queries during data-intensive parallel workflow execution. Future Generation Computer Systems, Elsevier, 75: 402-422, 2017.

[Silva Jr 2017] D. Silva Jr., A. Paes, E. Pacitti, D. Oliveira. Data Quality Prediction in Scientific Workflows. In preparation. 2017

[Souza 2017a] R. Souza, V. Silva, P. Miranda, A. Lima, P. Valduriez, M. Mattoso. Spark Scalability Analysis in a Scientific Workflow. Brazilian Symposium on Databases (SBBD), Best Paper Award, 2017.

[Souza 2017b] R. Souza, V. Silva, J. Camata, A. Coutinho, P. Valduriez, M. Mattoso. Tracking of Online Parameter Fine-tuning in Scientific Workflows. Workshop on Workflows in Support of Large-Scale Science (WORKS), ACM/IEEE Supercomputing Conference, 2017.

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