Purpose Numerous data quality (DQ) definitions in the form of sets of DQ dimensions are
found in the literature. The great differences across such DQ classifications (DQCs) imply a …

This book is the result of a study path that started in 2006, when the two authors of this book
published the book Data Quality: Concepts, Methodologies and Techniques. After 8 years …

As the challenge of our time, Big Data still has many research hassles, especially the variety
of data. The high diversity of data sources often results in information silos, a collection of …

Awareness of data and information quality issues has grown rapidly in light of the critical role
played by the quality of information in our data-intensive, knowledge-based economy …

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 …

Data warehouses have captured the attention of practitioners and researchers alike. But the
design and optimization of data warehouses remains an art rather than a science. This book …

This paper presents an approach and a tool to increase specification quality by using a
combination of UML and formal languages. Our approach is based on the expression of the …

Business process models are becoming available in large numbers due to their widespread
use in many industrial applications such as enterprise and quality engineering projects. On …

The rapid growth of the Web in the past two decades has made it the largest publicly
accessible data source in the world. Web mining aims to discover useful information or …

Data freshness has been identified as one of the most important data quality attributes in
information systems. This importance increases particularly in the context of distributed …