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 …

Computational Science and Engineering (CSE) projects are typically developed by
multidisciplinary teams. Despite being part of the same project, each team manages its own …

DfAnalyzer is a tool for monitoring, debugging, and analyzing dataflows generated by
Computational Science and Engineering (CSE) applications. It collects strategic raw data …

In the last few years, Apache Spark has become a de facto the standard framework for big
data systems on both industry and academy projects. Spark is used to execute compute‐and …

In long-lasting scientific workflow executions in HPC machines, computational scientists (the
users in this work) often need to fine-tune several workflow parameters. These tunings are …

In response to the increasing calls for algorithmic accountability, UML2PROV is a novel
approach to address the existing gap between application design, where models are …

Next-generation scientific applications in various fields are experiencing a rapid transition
from traditional experiment-based methodologies to large-scale computation-intensive …

Algoritmos de aprendizado de máquina pertencem a uma área em Inteligência Artificial que,
com base em análises sobre grandes conjuntos de dados,“aprendem” seu comportamento …

How to enable computational scientists and engineers to monitor and understand their
experiments when they are steering them on large-scale computers? This is the central …

Large-scale workflows that execute on High-Performance Computing machines need to be
dynamically steered by users. This means that users analyze big data files, assess key …