Abstract Machine learning (ML) models are nowadays used in complex applications in
various domains, such as medicine, bioinformatics, and other sciences. Due to their black …

Data visualization is a powerful skill for the demonstration of meaningful data insights in an
interactive and effective way. In this survey article, we collected 70 articles from last five …

In machine learning (ML), ensemble methods-such as bagging, boosting, and stacking-are
widely-established approaches that regularly achieve top-notch predictive performance …

This article presents a methodology defined as semantic modeling to create computable
virtual abstractions of complex manufacturing phenomena denoted as phenomena twins …

This paper addresses the construction of digital twins (exact mirror images of real-world in
cyberspace) using hidden Markov models for the futuristic manufacturing systems known as …

Demand is growing for more accountability regarding the technological systems that
increasingly occupy our world. However, the complexity of many of these systems—often …

Data provenance means recording data origins and the history of data generation and
processing. In healthcare, data provenance is one of the essential processes that make it …

There has been a considerable shift in the way how software is built and delivered today.
Most deployed software systems in modern times are created by (autonomous) distributed …

Provenance is recognized as a central challenge to establish the reliability and provide
security in computational systems. In scientific workflows, provenance is considered …

Data provenance has led to a develo** need for the technologies to empower end‐users
to assess and take action on the data life cycle. In the Big Data era, companies' amount of …