Data cleaning is frequently an iterative process tailored to the requirements of a specific
analysis task. The design and implementation of iterative data cleaning tools presents novel …

Analysts report spending upwards of 80% of their time on problems in data cleaning. The
data cleaning process is inherently iterative, with evolving cleaning workflows that start with …

Enterprises have been acquiring large amounts of data from a variety of sources to build
their own “Data Lakes”, with the goal of enriching their data asset and enabling richer and …

Data integration solutions dealing with large amounts of data have been strongly required in
the last few years. Besides the traditional data integration problems (eg schema integration …

The analyst effort in data cleaning is gradually shifting away from the design of hand-written
scripts to building and tuning complex pipelines of automated data cleaning libraries. Hyper …

Data collection has become a ubiquitous function of large organizations {not only for record
keeping, but to support a variety of data analysis tasks that are critical to the organizational …

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 …

Data cleaning techniques usually rely on some quality rules to identify violating tuples, and
then fix these violations using some repair algorithms. Oftentimes, the rules, which are …

As key decisions are often made based on information contained in a database, it is
important for the database to be as complete and correct as possible. For this reason, many …

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 …