Data and Information Quality

This book provides a systematic and comparative description of the vast number of research issues related to the quality of data and information. It does so by delivering a sound, integrated and comprehensive overview of the state of the art and future development of data and information quality in databases and information systems.

To this end, it presents an extensive description of the techniques that constitute the core of data and information quality research, including record linkage (also called object identification), data integration, error localization and correction, and examines the related techniques in a comprehensive and original methodological framework. Quality dimension definitions and adopted models are also analyzed in detail, and differences between the proposed solutions are highlighted and discussed. Furthermore, while systematically describing data and information quality as an autonomous research area, paradigms and influences deriving from other areas, such as probability theory, statistical data analysis, data mining, knowledge representation, and machine learning are also included. Last not least, the book also highlights very practical solutions, such as methodologies, benchmarks for the most effective techniques, case studies, and examples.

Introduction to Information Quality. - Data Quality Dimensions. - Information Quality Dimensions for Maps and Texts. - Data Quality Issues in Linked open data. - Quality Of Images. - Models for Information Quality. - Activities for Information Quality. - Object Identification. - Recent Advances in Object Identification. - Data Quality Issues in Data Integration Systems. - Information Quality in Use. - Methodologies for Information Quality Assessment and Improvement. - Information Quality in Healthcare. - Quality of Web Data and Quality of Big Data: Open Problems. - References. - Index.