Since a vast number of tables can be easily collected from web pages, spreadsheets, PDFs,
and various other document types, a flurry of table pre-training frameworks have been …

We propose TUTA, a unified pre-training architecture for understanding generally structured
tables. Noticing that understanding a table requires spatial, hierarchical, and semantic …

Tabular data are ubiquitous for the widespread applications of tables and hence have
attracted the attention of researchers to extract underlying information. One of the critical …

We contribute a deep-learning-based method that assists in designing analytical
dashboards for analyzing a data table. Given a data table, data workers usually need to …

Automatic Exploratory Data Analysis (EDA) focuses on automatically discovering pieces of
knowledge in the form of interesting data patterns. However, the conveyed knowledge by …

Tabular data have been playing a mostly important role in diverse real-world fields, such as
healthcare, engineering, finance, etc. With the recent success of deep learning, many …

It is common for people to create different types of charts to explore a multi-dimensional
dataset (table). However, to recommend commonly composed charts in real world, one …

Automatic visualization generates meaningful visualizations to support data analysis and
pattern finding for novice or casual users who are not familiar with visualization design …

Tables are a ubiquitous data format for insight communication. However, transforming data
into consumable tabular views remains a challenging and time-consuming task. To lower …

Data summarization is the process of producing interpretable and representative subsets of
an input dataset. It is usually performed following a one-shot process with the purpose of …