Fuzziness in Information Systems: How to Deal with Crisp and Fuzzy Data in Selection, Classification, and Summarization

ISBN-13: 9783319825984 / Angielski / Miękka / 2018 / 198 str.

Miroslav Hudec

Spis treści

1 Fuzzy Set and Fuzzy Logic Theory in Brief.- 2 Fuzzy Queries.- 3 Linguistic Summaries.- 4 Fuzzy Inference.- 5 Fuzzy Data in Relational Databases.- 6 Perspectives, Synergies and Conclusion.- A Illustrative Interfaces and Applications for Fuzzy Queries.- B Illustrative Interfaces and Applications for Linguistic Summaries. 

O autorze

Miroslav Hudec is researcher and teacher at the University of Economics in Bratislava, Slovakia. His research activities have been focused on information systems in official statistics and theory and applications of fuzzy logic, data mining and operations research. He is the author of approximately 45 scientific papers, a member of the program committee of several related international conferences and (currently) an editorial board member for Applied Soft Computing. In addition, he was the representative of Slovakia in the UNECE/Eurostat/OECD Conference on the Management of Statistical Information Systems from 2005 to 2009 and again in 2013.

Blurb

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.