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Neural networks cartridges for data mining on time series.

, , , and . IJCNN, page 2302-2309. IEEE Computer Society, (2009)

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Abstract: Using Provenance to Visualize Data from Large-Scale Experiments., , , , , and . SC Companion, page 1418-1419. IEEE Computer Society, (2012)An adaptive parallel execution strategy for cloud-based scientific workflows., , , , and . Concurrency and Computation: Practice and Experience, 24 (13): 1531-1550 (2012)Towards a UML-based Reference Model for Blended Learning., , , , and . iJES, 2 (3): 15-22 (2014)Constellation Queries over Big Data., , , , , and . CoRR, (2017)Nonstationary time series transformation methods: An experimental review., , , , and . Knowl.-Based Syst., (2019)Rumo à Integração da Álgebra de Workflows com o Processamento de Consulta Relacional., , , , , and . SBBD, page 205-210. SBC, (2018)Discovering Tight Space-Time Sequences., , , , , , and . DaWaK, volume 11031 of Lecture Notes in Computer Science, page 247-257. Springer, (2018)On Evaluating Data Preprocessing Methods for Machine Learning Models for Flight Delays., , , , and . IJCNN, page 1-8. IEEE, (2018)Poster: scientific data parallelism using P2P technique., , , and . SC Companion, page 27-28. ACM, (2011)A P2P Approach to Many Tasks Computing for Scientific Workflows., , , , , , , , and . VECPAR, volume 6449 of Lecture Notes in Computer Science, page 327-339. Springer, (2010)