Data science is a rapidly growing discipline and organizations increasingly depend on data
science work. Yet the ambiguity around data science, what it is, and who data scientists are …

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

In the last two decades, interactive visualization and analysis have become a central tool in
data-driven decision making. Concurrently to the contributions in data visualization …

Dataframes are a popular abstraction to represent, prepare, and analyze data. Despite the
remarkable success of dataframe libraries in Rand Python, dataframes face performance …

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 …

Analytical dashboards are popular in business intelligence to facilitate insight discovery with
multiple charts. However, creating an effective dashboard is highly demanding, which …

Reverse engineering is a complex process essential to software-security tasks such as
vulnerability discovery and malware analysis. Significant research and engineering effort …

Exploratory Data Analysis (EDA) is a crucial step in any data science project. However,
existing Python libraries fall short in supporting data scientists to complete common EDA …

In 2020 the world has generating 52 times the amount of data as in 2010, and 76 times the
number of information sources. Being able to use this data provides huge opportunities and …

Although we have seen a proliferation of algorithms for recommending visualizations, these
algorithms are rarely compared with one another, making it difficult to ascertain which …