SaLLy - Big Data

Big data refers to extremely large, complex, and diverse sets of data that cannot be easily processed or analyzed using traditional data processing techniques. It is typically characterized by the "three Vs": volume (the size of the data), velocity (the speed at which the data is generated and must be processed), and variety (the different forms and formats of the data). Big data is generated from a variety of sources, including social media, sensors, mobile devices, and other digital technologies. It is used by organizations and businesses to gain insights and make data-driven decisions that can lead to improved efficiency, productivity, and customer satisfaction. However, working with big data requires specialized tools, techniques, and expertise, such as data mining, machine learning, and artificial intelligence. In this topic, we are interested in directions such as:

Applied Statistics: The development of applications to promote the solution of everyday problems involving methodologies for statistical applications and the teaching of Statistics and Experimental Statistics.

Computational Modeling: The creation of computational models to perform simulations to solve problems in the areas of Epidemiology, Animal Health, and Artificial Intelligence.

Natural Language Processing: The creation of methods and applications focused on textual analysis, involving statistics and artificial intelligence, of data generated on social networks and from different textual sources.

Data Science: The development of computational techniques, involving statistics, computing, and business rules to point out solutions for problems involving massive parallel processing.

Meet Our Big Data Team

Cristtian Paixão

Cristtyan Paixão

Research Coordinator & Team Leader

Key Publications

- Rodrigues, P. C., & Carfagna, E. (2023). Data science applied to environmental sciences. Environmetrics, 34(1), e2783.

- Sánchez-Mora, F. D., Saifert, L., Zanghelini, J., Paixão, C. A., Vesco, L. L. D., Eibach, R., ... & Welter, L. J. (2023). Pyramiding of resistance alleles to grape powdery mildew assisted by molecular markers. Crop Breeding and Applied Biotechnology, 22.

- Campos, K. A., Morais, A. R., & Paixão, C. A. (2020). Viabilidade do uso da Função Discriminante de Fisher: comparação com a manava. Brazilian Journal of Biometrics, 38(2), 159-184.

- Baccin, C. R. A., Dal Sasso, G. T. M., Paixão, C. A., & de Sousa, P. A. F. (2020). Mobile application as a learning aid for nurses and nursing students to identify and care for stroke patients: Pretest and posttest results. CIN: Computers, Informatics, Nursing, 38(7), 358-366.

- França, A. R. D. S., Borcioni, E., Paixão, C. A., Araújo, R. S., CAMPOS, R. V. D., & CRUZ, S. P. D. (2020). Growth and yield of carrot inoculated with Bacillus subtilis and Pseudomonas fluorescens. Revista Colombiana de Ciencias Hortícolas, 14(3), 385-392.

The complete list of publications from SaLLy members can be found in our Google Scholar profile.