Creating and running software produces large amounts of raw data about the development
process and the customer usage, which can be turned into actionable insight with the help of …

In recent years, machine learning, especially deep learning, has developed rapidly and has
shown remarkable performance in many tasks of the smart grid field. The representation …

Machine learning (ML) has become increasingly influential to human society, yet the primary
advancements and applications of ML are driven by research in only a few computational …

Organizations rely on machine learning engineers (MLEs) to operationalize ML, ie, deploy
and maintain ML pipelines in production. The process of operationalizing ML, or MLOps …

Supporting exploratory visual analysis (EVA) is a central goal of visualization research, and
yet our understanding of the process is arguably vague and piecemeal. We contribute a …

Software developers are increasingly required to write big data code. However, they find big
data software development challenging. To help these developers it is necessary to …

Various case studies in different application domains have shown the great potential of
visual parameter space analysis to support validating and using simulation models. In order …

Immersive analytics turns the very space surrounding the user into a canvas for data
analysis, supporting human cognitive abilities in myriad ways. We present the results of a …

Today, most conversational agents are limited to simple tasks supported by standalone
commands, such as getting directions or scheduling an appointment. To support more …

Data analysis is challenging as analysts must navigate nuanced decisions that may yield
divergent conclusions. AI assistants have the potential to support analysts in planning their …