@inproceedings{BravoSELK22,
  author    = {Fl{\'a}via Bravo and Luciana Sousa and Tatiana Escovedo and Helio Lopes and Marcos Kalinowski},
  title     = {Machine Learning Aplicado à Predi{\c{c}}{\~a}o da Obriga{\c{c}}{\~a}o de Investimento em P, D & I},
  abstract  = {Investments in Research, Development, and Innovation (R&D&I) from Brazil's oil and gas sector are substantial due to the obligation established by the National Agency of Petroleum, Natural Gas and Biofuels (ANP). Identifying the expectation of funding in an agile and simple way enables better planning, increasing the effectiveness of expenditures. This article proposes elaborating a machine learning model to predict the potential of mandatory investments that companies in the oil and gas sector must make in R&D&I, allowing better planning of the application of financial resources for universities and science and technology institutes.},
  booktitle = {Brazilian Symposium on Databases, {SBBD} 2022, Buzios, Brazil, Sept 19-23},
  pages     = {1-13},
  year      = {2022},
  urlAuthor_version = {http://www.inf.puc-rio.br/~kalinowski/publications/BravoSELK22.pdf},
  doi       = {10.5753/sbbd.2022.224344}
}

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