Data visualization is crucial in today's data-driven business world, which has been widely
used for helping decision making that is closely related to major revenues of many industrial …

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 …

Data science requires time-consuming iterative manual activities. In particular, activities
such as data selection, preprocessing, transformation, and mining, highly depend on …

Data quality poses an important challenge to corporate data management and is a critical
success factor for organizations. A lack of it can deteriorate business operations and impair …

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 …

Researchers collect large amounts of user interaction data with the goal of mapping user's
workflows and behaviors to their high‐level motivations, intuitions, and goals. Although the …

Leaving the context of visualizations invisible can have negative impacts on understanding
and transparency. While common wisdom suggests that recontextualizing visualizations with …

The problem of data visualization is to transform data into a visual context such that people
can easily understand the significance of data. Nowadays, data visualization becomes …

In this paper, we present a new benchmark to validate the suitability of database systems for
interactive visualization workloads. While there exist proposals for evaluating database …

Latency in a visualization system is widely believed to affect user behavior in measurable
ways, such as requiring the user to wait for the visualization system to respond, leading to …