The explorative and iterative nature of developing and operating ML applications leads to a
variety of artifacts, such as datasets, features, models, hyperparameters, metrics, software …

Machine learning (ML) has widespread applications and has revolutionized many
industries, but suffers from several challenges. First, sufficient high-quality training data is …

To trust findings in computational science, scientists need workflows that trace the data
provenance and support results explainability. As workflows become more complex, tracing …

Data provenance, or data lineage, describes the life cycle of data. In scientific workflows on
HPC systems, scientists often seek diverse provenance (eg, origins of data products, usage …

The scanpath is an important concept in eye tracking. It refers to a person's eye movements
over a period of time, commonly represented as a series of alternating fixations and …

A Deep Learning (DL) life cycle involves several data transformations, such as performing
data pre-processing, defining datasets to train and test a deep neural network (DNN), and …

Business users perform data analysis to inform decisions for improving business processes
and outcomes despite having limited formal technical training. While earlier work has …

Supporting iterative and explorative workflows for developing machine learning (ML)
models, ML experiment management systems (ML EMSs), such as MLflow, are increasingly …

Data lineage allows information to be traced to its origin in data analysis by showing how the
results were derived. Although many methods have been proposed to identify the source …

Modern scientific workflows require hybrid infrastructures combining numerous
decentralized resources on the IoT/Edge interconnected to Cloud/HPC systems (aka the …