Traceability is considered a key requirement for trustworthy artificial intelligence (AI), related
to the need to maintain a complete account of the provenance of data, processes, and …

Data lakes are a fundamental building block for many industrial data analysis solutions and
becoming increasingly popular in research. Often associated with big data use cases, data …

Data provenance, a record that describes the origins and processing of data, offers new
promises in the increasingly important role of artificial intelligence (AI)-based systems in …

The act of sharing scientific knowledge is rapidly evolving away from traditional articles and
presentations to the delivery of executable objects that integrate the data and computational …

Identifying the root cause and impact of a system intrusion remains a foundational challenge
in computer security. Digital provenance provides a detailed history of the flow of information …

Large-scale datasets present unique opportunities to perform scientific investigations with
unprecedented breadth. However, they also pose considerable challenges for the findability …

PROV-TEMPLATEIS a declarative approach that enables designers and programmers to
design and generate provenance compatible with the PROV standard of the World Wide …

Despite the wide usage of container-based cloud computing, container auditing for security
analysis relies mostly on built-in host audit systems, which often lack the ability to capture …

The Deep Learning (DL) workflow involves several steps of data transformation. Evaluating
various configurations at each step of the workflow may be a complex task when it comes to …

Network functions virtualization (NFV) enables agile deployment of network services on top
of clouds. However, as NFV involves multiple levels of abstraction representing the same …