The State-of-the-Art of Business Intelligence and Data Mining in the Context of Grid and Utility Computing: A PRISMA Systematic Review

Resume

This work is cataloged in the Scientific Portal. Officially published in 10th International Conference on Software Process Improvement (CIMPS 2021) (2022). Referenced as: "Ernani Damasceno; Ana Azevedo; Manuel Perez-Cota".

The complete abstract and full text can be accessed directly at the official DOI: https://doi.org/10.1007/978-3-030-89909-7_7

Bibliographic & Editorial Details

Venue / Journal / Book 10th International Conference on Software Process Improvement (CIMPS 2021)

Publisher Springer International Publishing

Pagination Pages 83 - 96

Role Coauthor

Official Identifiers

DOI: 10.1007/978-3-030-89909-7_7

Autores & Descoberta de Ciência ID

Identificação dos autores da instituição e deteção de coautores via FCT Ciência Vitae.

Autor(es) da Instituição

A

Ana Isabel Rojão Lourenço Azevedo CEOS.PP

Ciência ID: D913-646F-CE08

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E

Ernani Vinicius Damasceno Investigador

Ciência ID: 6811-6996-747B

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Coautores da Obra

Autores discriminados na citação da publicação:

Ernani Damasceno; Ana Azevedo; Manuel Perez-Cota

Clique no botão acima para consultar a API da FCT e descobrir os perfis Ciência ID dos coautores.

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The State-of-the-Art of Business Intelligence and Data Mining in the Context of Grid and Utility Computing: A PRISMA Systematic Review 🔓

Resume

Bibliographic & Editorial Details

Official Identifiers

Autores & Descoberta de Ciência ID

Nenhum coautor adicional com Ciência ID associado

The State-of-the-Art of Business Intelligence and Data Mining in the Context of Grid and Utility Computing: A PRISMA Systematic Review 🔓

Resume

Bibliographic & Editorial Details

Official Identifiers

Autores & Descoberta de Ciência ID

Nenhum coautor adicional com Ciência ID associado

Other Related Publications

AI-Driven Predictive Model for Diabetes Risk Assessment - A Class-Balanced Machine Learning Approach with Web-Based Deployment

SiSchool: A QR-Based Mobile Device Control System for Educational Environments

From predicting dropout rates to responsible intervention: explainable machine learning for academic success in higher education

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