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 …

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 …

Database users commonly explore large data sets by running aggregate queries that project
the data down to a smaller number of points and dimensions, and visualizing the results …

Data quality is one of the most important problems in data management, since dirty data
often leads to inaccurate data analytics results and wrong business decisions. Poor data …

As a consequence of the popularity of big data, many users with a variety of backgrounds
seek to extract high level information from datasets collected from various sources and …

Data quantity and data quality, like two sides of a coin, are equally important to data
management. This paper provides an overview of recent advances in the study of data …

A lot of systems and applications are data-driven, and the correctness of their operation
relies heavily on the correctness of their data. While existing data cleaning techniques can …

Data provenance has evolved from a niche topic to a mainstream area of research in
databases and other research communities. This article gives a comprehensive introduction …

In light of the growing popularity of Exploratory Data Analysis (EDA), understanding the
underlying causes of the knowledge acquired by EDA is crucial. However, it remains under …

Data quality is one of the most important problems in data management, since dirty data
often leads to inaccurate data analytics results and wrong business decisions. Data cleaning …