It is extremely difficult to track down the original source of sensitive data from a variety of
sources in the cloud during transit and processing. For instance, data provenance, which …

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

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

Computer simulations consume and produce huge amounts of raw data files presented in
different formats, eg, HDF5 in computational fluid dynamics simulations. Users often need to …

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 …

When exploring big amounts of data without a clear target, providing an interactive
experience becomes really difficult, since this tentative inspection usually defeats any early …

Scientific workflows need to be iteratively, and often interactively, executed for large input
datasets. Reducing data from input datasets is a powerful way to reduce overall execution …

Data is a critical element in any discovery process. In the last decades, we observed
exponential growth in the volume of available data and the technology to manipulate it …

Cross-matching data stored on separate files is an everyday activity in the scientific domain.
However, sometimes the relation between attributes may not be obvious. The discovery of …

(AMR) are optimal strategies for tackling large-scale simulations. libMesh is an open-source
finite-element library that supports parallel AMR and is used in multiphysics applications. In …