As big data becomes ubiquitous across domains, and more and more stakeholders aspire to
make the most of their data, demand for machine learning tools has spurred researchers to …

Detecting and repairing dirty data is one of the perennial challenges in data analytics, and
failure to do so can result in inaccurate analytics and unreliable decisions. Over the past few …

Text-to-SQL parsing, which aims at converting natural language instructions into executable
SQLs, has gained increasing attention in recent years. In particular, GPT-4 and Claude-2 …

This is an overview of the end-to-end data cleaning process. Data quality is one of the most
important problems in data management, since dirty data often leads to inaccurate data …

Modern artificial intelligence (AI) applications require large quantities of training and test
data. This need creates critical challenges not only concerning the availability of such data …

We introduce HoloClean, a framework for holistic data repairing driven by probabilistic
inference. HoloClean unifies existing qualitative data repairing approaches, which rely on …

Equipping machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …

Modern companies and institutions rely on data to guide every single business process and
decision. Missing or incorrect information seriously compromises any decision process …

Entity matching (EM) identifies data instances that refer to the same real-world entity, such
as (David Smith, UWMadison) and (DM Smith, UWM). This problem has been a long …

Data cleaning has played a critical role in ensuring data quality for enterprise applications.
Naturally, there has been extensive research in this area, and many data cleaning …