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This book is an essential contribution to the description of
fuzziness in information systems. Usually users want to retrieve
data or summarized information from a database and are interested
in classifying it or building rule-based systems on it. But they
are often not aware of the nature of this data and/or are unable to
determine clear search criteria. The book examines theoretical and
practical approaches to fuzziness in information systems based on
statistical data related to territorial units. Chapter 1 discusses
the theory of fuzzy sets and fuzzy logic to enable readers to
understand the information presented in the book. Chapter 2 is
devoted to flexible queries and includes issues like constructing
fuzzy sets for query conditions, and aggregation operators for
commutative and non-commutative conditions, while Chapter 3 focuses
on linguistic summaries. Chapter 4 presents fuzzy logic control
architecture adjusted specifically for the aims of business and
governmental agencies, and shows fuzzy rules and procedures for
solving inference tasks. Chapter 5 covers the fuzzification of
classical relational databases with an emphasis on storing fuzzy
data in classical relational databases in such a way that existing
data and normal forms are not affected. This book also examines
practical aspects of user-friendly interfaces for storing,
updating, querying and summarizing. Lastly, Chapter 6 briefly
discusses possible integration of fuzzy queries, summarization and
inference related to crisp and fuzzy databases. The main target
audience of the book is researchers and students working in the
fields of data analysis, database design and business intelligence.
As it does not go too deeply into the foundation and mathematical
theory of fuzzy logic and relational algebra, it is also of
interest to advanced professionals developing tailored applications
based on fuzzy sets.
This book is an essential contribution to the description of
fuzziness in information systems. Usually users want to retrieve
data or summarized information from a database and are interested
in classifying it or building rule-based systems on it. But they
are often not aware of the nature of this data and/or are unable to
determine clear search criteria. The book examines theoretical and
practical approaches to fuzziness in information systems based on
statistical data related to territorial units. Chapter 1 discusses
the theory of fuzzy sets and fuzzy logic to enable readers to
understand the information presented in the book. Chapter 2 is
devoted to flexible queries and includes issues like constructing
fuzzy sets for query conditions, and aggregation operators for
commutative and non-commutative conditions, while Chapter 3 focuses
on linguistic summaries. Chapter 4 presents fuzzy logic control
architecture adjusted specifically for the aims of business and
governmental agencies, and shows fuzzy rules and procedures for
solving inference tasks. Chapter 5 covers the fuzzification of
classical relational databases with an emphasis on storing fuzzy
data in classical relational databases in such a way that existing
data and normal forms are not affected. This book also examines
practical aspects of user-friendly interfaces for storing,
updating, querying and summarizing. Lastly, Chapter 6 briefly
discusses possible integration of fuzzy queries, summarization and
inference related to crisp and fuzzy databases. The main target
audience of the book is researchers and students working in the
fields of data analysis, database design and business intelligence.
As it does not go too deeply into the foundation and mathematical
theory of fuzzy logic and relational algebra, it is also of
interest to advanced professionals developing tailored applications
based on fuzzy sets.
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