Neural networks are important tools for data-intensive analysis and are commonly applied to
model non-linear relationships between dependent and independent variables. However …

Modern scientific applications read and write massive amounts of data through simulations,
observations, and analysis. These applications spend the majority of their runtime in …

Constructing the reliable dynamic sensitivity profile for the output variable using the machine
learning model is a challenging task; however, the dynamic sensitivity trends are helpful to …

The amount of biomedical data collected and stored has grown significantly. Analyzing
these extensive amounts of data cannot be done by individuals or single organizations …

Improvements in I/O architectures are becoming increasingly required nowadays. This is an
essential point to complex and data intensive scalable applications. Data-Intensive Scalable …

In recent years, the amount of biomedical data collected and stored has grown significantly.
Analyzing these extensive amounts of data can no longer be done by individuals or single …

Traditional compute-centric scientific discovery has led to a growing gap between
computation power and storage capabilities. However, in the data explosion era, where data …

Este trabalho apresenta uma introdução à abordagem de Computação Escalável Intensiva
de Dados (DISC). Este paradigma representa um esforço valioso para lidar com a grande …