Visual analytics systems combine machine learning or other analytic techniques with
interactive data visualization to promote sensemaking and analytical reasoning. It is through …

With the ever-increasing amount of data, the world has stepped into the era of “Big Data”.
Presently, the analysis of massive and complex data and the extraction of relevant …

Code-generating large language models map natural language to code. However, only a
small portion of the infinite space of naturalistic utterances is effective at guiding code …

Dimensionality Reduction (DR) is a core building block in visualizing multidimensional data.
For DR techniques to be useful in exploratory data analysis, they need to be adapted to …

There is fast‐growing literature on provenance‐related research, covering aspects such as
its theoretical framework, use cases, and techniques for capturing, visualizing, and …

Visual analytic tools combine the complementary strengths of humans and machines in
human-in-the-loop systems. Humans provide invaluable domain expertise and …

Visual analytics emphasizes sensemaking of large, complex datasets through interactively
exploring visualizations generated by statistical models. For example, dimensionality …

Visual analytics is the science of marrying interactive visualizations and analytic algorithms
to support exploratory knowledge discovery in large datasets. We argue for a shift from a …

The world's corpora of data grow in size and complexity every day, making it increasingly
difficult for experts to make sense out of their data. Although machine learning offers …

An increasing number of interactive visualization tools stress the integration with
computational software like MATLAB and R to access a variety of proven algorithms. In …