Ability to track provenance is a key feature of scientific workflows to support data lineage and
reproducibility. The challenges that are introduced by the volume, variety and velocity of Big …

Provenance refers to the entire amount of information, comprising all the elements and their
relationships, that contribute to the existence of a piece of data. The knowledge of …

Provenance refers to any information describing the production process of an end product,
which can be anything from a piece of digital data to a physical object. While this survey …

Machine learning (ML) is now commonplace, powering data-driven applications in various
organizations. Unlike the traditional perception of ML in research, ML production pipelines …

Debugging data processing logic in Data-Intensive Scalable Computing (DISC) systems is a
difficult and time consuming effort. Today's DISC systems offer very little tooling for …

Data provenance has evolved from a niche topic to a mainstream area of research in
databases and other research communities. This article gives a comprehensive introduction …

Imagine a computational process that uses a complex input consisting of multiple" items"(eg,
files, tables, tuples, parameters, configuration rules) The provenance analysis of such a …

Data science has become prevalent in a large variety of domains. Inherent in its practice is
an exploratory, probing, and fact finding journey, which consists of the assembly, adaptation …

A lot of systems and applications are data-driven, and the correctness of their operation
relies heavily on the correctness of their data. While existing data cleaning techniques can …

Internet of Things (IoT) has become an emerging trend in the information and engineering
industries, which is inevitable to impact our lives in various ways. The advancement of IoT as …