ORIGINAL ARTICLE
Convolutional neural networks applied to data organized as OLAP cubes
Rodrigo Ribeiro Caputo,
Edimilson Batista dos Santos,
Leonardo Chaves Dutra da Rocha,
Rodrigo Ribeiro Caputo
Department of Computer Science (DCOMP), Federal University of São João del-Rei (UFSJ), São João del-Rei, Brazil
Search for more papers by this authorCorresponding Author
Edimilson Batista dos Santos
Department of Computer Science (DCOMP), Federal University of São João del-Rei (UFSJ), São João del-Rei, Brazil
Correspondence
Edimilson Batista dos Santos, Department of Computer Science, Federal University of Sao João del Rei, Av. Visconde do Rio Preto, s/n°, Colônia do Bengo, São João del-Rei, MG CEP 36301-360, Brazil.
Email: [email protected]
Search for more papers by this authorLeonardo Chaves Dutra da Rocha
Department of Computer Science (DCOMP), Federal University of São João del-Rei (UFSJ), São João del-Rei, Brazil
Search for more papers by this authorRodrigo Ribeiro Caputo,
Edimilson Batista dos Santos,
Leonardo Chaves Dutra da Rocha,
Rodrigo Ribeiro Caputo
Department of Computer Science (DCOMP), Federal University of São João del-Rei (UFSJ), São João del-Rei, Brazil
Search for more papers by this authorCorresponding Author
Edimilson Batista dos Santos
Department of Computer Science (DCOMP), Federal University of São João del-Rei (UFSJ), São João del-Rei, Brazil
Correspondence
Edimilson Batista dos Santos, Department of Computer Science, Federal University of Sao João del Rei, Av. Visconde do Rio Preto, s/n°, Colônia do Bengo, São João del-Rei, MG CEP 36301-360, Brazil.
Email: [email protected]
Search for more papers by this authorLeonardo Chaves Dutra da Rocha
Department of Computer Science (DCOMP), Federal University of São João del-Rei (UFSJ), São João del-Rei, Brazil
Search for more papers by this authorFirst published: 19 September 2023
Abstract
This paper presents a Convolutional Neural Network (CNN) architecture named OlapNet, which incorporates implicit operations of OLAP cubes (or data cubes). OLAP cubes are produced from database tables or spreadsheets and they allow particular operations that support performing complex queries efficiently. OlapNet permits evaluating various combinations of these OLAP operations in its search space and thus, it enables, in part, to automate the data transformation step in the knowledge discovery process. A sample of data from an actual database containing anonymized data on the debt history of customers of a financial institution has been used to evaluate our proposal. A predictive classification problem to estimate the probability of any given customer contracting new credits in the next three months has been modelled from these data. Then, traditional methods of Machine Learning and CNN were applied. The results showed that CNN, using the OlapNet architecture, outperforms traditional methods in almost all cases, indicating that the proposed architecture is quite promising.
Open Research
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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