000 04028nam a22005175i 4500
001 978-3-319-42518-4
003 DE-He213
005 20200421111705.0
007 cr nn 008mamaa
008 160928s2016 gw | s |||| 0|eng d
020 _a9783319425184
_9978-3-319-42518-4
024 7 _a10.1007/978-3-319-42518-4
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
082 0 4 _a006.312
_223
100 1 _aHudec, Miroslav.
_eauthor.
245 1 0 _aFuzziness in Information Systems
_h[electronic resource] :
_bHow to Deal with Crisp and Fuzzy Data in Selection, Classification, and Summarization /
_cby Miroslav Hudec.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXXII, 198 p. 91 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _a1 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. .
520 _aThis 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.
650 0 _aComputer science.
650 0 _aMathematical logic.
650 0 _aData mining.
650 0 _aArtificial intelligence.
650 0 _aComputational intelligence.
650 1 4 _aComputer Science.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aComputational Intelligence.
650 2 4 _aInformation Systems Applications (incl. Internet).
650 2 4 _aMathematical Logic and Formal Languages.
650 2 4 _aArtificial Intelligence (incl. Robotics).
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319425160
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-42518-4
912 _aZDB-2-SCS
942 _cEBK
999 _c55210
_d55210