Data and Information Quality

Dimensions, Principles and Techniques

  • Carlo Batini
  • Monica Scannapieco

Part of the Data-Centric Systems and Applications book series (DCSA)

Table of contents

  1. Front Matter
    Pages i-xxviii
  2. Carlo Batini, Monica Scannapieco
    Pages 1-19
  3. Carlo Batini, Monica Scannapieco
    Pages 21-51
  4. Carlo Batini, Monica Scannapieco
    Pages 53-86
  5. Anisa Rula, Andrea Maurino, Carlo Batini
    Pages 87-112
  6. Gianluigi Ciocca, Silvia Corchs, Francesca Gasparini, Carlo Batini, Raimondo Schettini
    Pages 113-135
  7. Carlo Batini, Monica Scannapieco
    Pages 137-154
  8. Carlo Batini, Monica Scannapieco
    Pages 155-175
  9. Carlo Batini, Monica Scannapieco
    Pages 177-215
  10. Carlo Batini, Monica Scannapieco
    Pages 217-277
  11. Carlo Batini, Monica Scannapieco
    Pages 279-307
  12. Carlo Batini, Monica Scannapieco
    Pages 309-352
  13. Carlo Batini, Monica Scannapieco
    Pages 353-402
  14. Federico Cabitza, Carlo Batini
    Pages 403-419
  15. Monica Scannapieco, Laure Berti
    Pages 421-449
  16. Back Matter
    Pages 451-500

About this book


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.

The book has been written primarily for researchers in the fields of databases and information management or in natural sciences who are inte

rested in investigating properties ofdata and information that have an impact on the quality of experiments, processes and on real life. The material presented is also sufficiently self-contained for masters or PhD-level courses, and it covers all the fundamentals and topics without the need for other textbooks. Data and information system administrators and practitioners, who deal with systems exposed to data-quality issues and as a result need a systematization of the field and practical methods in the area, will also benefit from the combination of concrete practical approaches with sound theoretical formalisms.


Data Integration Data Quality Data Provenance Data Analysis Information Quality Object Identification Web Data Management Integrity Checking Information Integration Health Care Information Systems

Authors and affiliations

  • Carlo Batini
    • 1
  • Monica Scannapieco
    • 2
  1. 1.di Milano-BiccocaUniversità degli StudiMilanItaly
  2. 2.'La Sapienza'Università degli Studi di RomaRomeItaly

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science Computer Science (R0)
  • Print ISBN 978-3-319-24104-3
  • Online ISBN 978-3-319-24106-7
  • Series Print ISSN 2197-9723
  • Series Online ISSN 2197-974X
  • Buy this book on publisher's site