Data quality is one of the most important problems in data management. A database system
typically aims to support the creation maintenance and use of large amount of data focusing
on the quantity of data. However real-life data are often dirty: inconsistent duplicated
inaccurate incomplete or stale. Dirty data in a database routinely generate misleading or
biased analytical results and decisions and lead to loss of revenues credibility and
customers. With this comes the need for data quality management. In contrast to traditional
data management tasks data quality management enables the detection and correction of errors
in the data syntactic or semantic in order to improve the quality of the data and hence add
value to business processes. While data quality has been a longstanding problem for decades
the prevalent use of the Web has increased the risks on an unprecedented scale of creating
and propagating dirty data. This monograph gives an overview of fundamental issues underlying
central aspects of data quality namely data consistency data deduplication data accuracy
data currency and information completeness. We promote a uniform logical framework for dealing
with these issues based on data quality rules. The text is organized into seven chapters
focusing on relational data. Chapter One introduces data quality issues. A conditional
dependency theory is developed in Chapter Two for capturing data inconsistencies. It is
followed by practical techniques in Chapter 2b for discovering conditional dependencies and
for detecting inconsistencies and repairing data based on conditional dependencies. Matching
dependencies are introduced in Chapter Three as matching rules for data deduplication. A
theory of relative information completeness is studied in Chapter Four revising the classical
Closed World Assumption and the Open World Assumption to characterize incomplete information
in the real world. A data currency model is presented in Chapter Five to identify the current
values of entities in a database and to answer queries with the current values in the absence
of reliable timestamps. Finally interactions between these data quality issues are explored in
Chapter Six. Important theoretical results and practical algorithms are covered but formal
proofs are omitted. The bibliographical notes contain pointers to papers in which the results
were presented and proven as well as references to materials for further reading. This text is
intended for a seminar course at the graduate level. It is also to serve as a useful resource
for researchers and practitioners who are interested in the study of data quality. The
fundamental research on data quality draws on several areas including mathematical logic
computational complexity and database theory. It has raised as many questions as it has
answered and is a rich source of questions and vitality. Table of Contents: Data Quality: An
Overview Conditional Dependencies Cleaning Data with Conditional Dependencies Data
Deduplication Information Completeness Data Currency Interactions between Data Quality
Issues
