activity
20192023
most citedWorkflow Provenance in the Lifecycle of Scientific Machine Learning

44 citations · 52 across the 6 of their papers we have counts for

collaborators

6 papers

cs.DB2023

A Polystore Architecture Using Knowledge Graphs to Support Queries on Heterogeneous Data Stores

Leonardo Guerreiro Azevedo, Renan Francisco Santos Souza, Elton F. de S. Soares +4

Modern applications commonly need to manage dataset types composed of heterogeneous data and schemas, making it difficult to access them in an integrated way. A single data store t…

cs.SE2021★ 8 cited

DevOps and Microservices in Scientific System development

Maximillien de Bayser, Vinicius Segura, Leonardo Guerreiro Azevedo +4

There is a gap in scientific information systems development concerning modern software engineering and scientific computing. Historically, software engineering methodologies have…

cs.SE2021

DevOps Adoption: Eight Emergent Perspectives

Mauro Lourenço Pedra, Mônica Ferreira da Silva, Leonardo Guerreiro Azevedo

DevOps is an approach based on lean and agile principles in which business, development, operations, and quality teams cooperate to deliver software continuously aiming at reducing…

cs.DB2020★ 44 cited

Workflow Provenance in the Lifecycle of Scientific Machine Learning

Renan Souza, Leonardo G. Azevedo, Vítor Lourenço +10

Machine Learning (ML) has already fundamentally changed several businesses. More recently, it has also been profoundly impacting the computational science and engineering domains,…

cs.DB2020

Managing Data Lineage of O&G Machine Learning Models: The Sweet Spot for Shale Use Case

Raphael Thiago, Renan Souza, L. Azevedo +7

Machine Learning (ML) has increased its role, becoming essential in several industries. However, questions around training data lineage, such as "where has the dataset used to trai…

cs.DC2019

Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering

Renan Souza, Leonardo Azevedo, Vítor Lourenço +10

Machine Learning (ML) has become essential in several industries. In Computational Science and Engineering (CSE), the complexity of the ML lifecycle comes from the large variety of…