Real-time collaboration has become increasingly important in various applications, from
document creation to data analytics. Although collaboration features are prevalent in editing …

Data transformation is an essential step in data science. While experts primarily use
programming to transform their data, there is an increasing need to support non …

This systematic review critically evaluates the nexus between data-driven design (DDD) and
design processes (DP), guided by PRISMA standards. It synthesises findings from 84 …

Exploratory Data Analysis (EDA) provides guidance to users to help them refine their needs
and find items of interest in large volumes of structured data. In this paper, we develop …

For a nationwide logistics transportation system, it is critical to make the vehicle loading
plans (ie, given many packages, deciding vehicle types and numbers) at each sorting and …

The fundamental goal of business data analysis is to improve business decisions using
data. Business users often make decisions to achieve key performance indicators (KPIs) …

Current computational notebooks, such as Jupyter, are a popular tool for data science and
analysis. However, they use a 1D list structure for cells that introduces and exacerbates user …

Organizations need to ensure the quality of data that is used for analytics and to maintain its
consistency across multiple analytical and operational systems. Master data is a term that …

In data exploration, users need to analyze large data files quickly, aiming to minimize data-to-
analysis time. While recent adaptive indexing approaches address this need, they are cases …

GUI-based data processing systems simplify and accelerate data tasks with a user-friendly
interface, eliminating the need for extensive coding skills. This accessibility allows analysts …