We classify data quality problems that are addressed by data cleaning and provide an
overview of the main solution approaches. Data cleaning is especially required when …

The development of the Internet in recent years has made it possible and useful to access
many different information systems anywhere in the world to obtain information. While there …

Abstract Machine learning (ML) has become a vital part in many aspects of our daily life.
However, building well performing machine learning applications requires highly …

This textbook explores the different aspects of data mining from the fundamentals to the
complex data types and their applications, capturing the wide diversity of problem domains …

When a program fails to process an input, it need not be the program code that is at fault. It
can also be that the input data is faulty, for instance as result of data corruption. To get the …

Though data analysis tools continue to improve, analysts still expend an inordinate amount
of time and effort manipulating data and assessing data quality issues. Such" data …

Cleaning data of errors in structure and content is important for data warehousing and
integration. Current solutions for data cleaning involve many iterations of data “auditing” to …

Entity matching is a crucial and difficult task for data integration. Entity matching frameworks
provide several methods and their combination to effectively solve different match tasks. In …

In spite of advances in technologies for working with data, analysts still spend an inordinate
amount of time diagnosing data quality issues and manipulating data into a usable form …

Extraction-Transformation-Loading (ETL) tools are pieces of software responsible for the
extraction of data from several sources, their cleansing, customization and insertion into a …