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008			160928s2016 gw | s |||| 0|eng d
020			_a9783319425184
040			_aES-MaUEC
050		4	_aQA76.9.D343 _bH834 2016 EB
082	0	4	_a006.312
100	1		_aHudec, Miroslav _999879 _0Local
245	1	0	_aFuzziness in Information Systems : _bHow to Deal with Crisp and Fuzzy Data in Selection, Classification, and Summarization _cby Miroslav Hudec
260			_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XXII, 198 p.) _b91 ilustraciones
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
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	3		_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
710	2		_aSpringerLink (Online service) _0Local _9106996
942			_2lcc _cLE
988			_aEBOOK, EBSPRINGER
650		7	_aData mining _0comprobar BNE20033218554 _2embne _9162648
650		7	_aLógica matemática _0comprobar BNE19900968744 _2embne _9139136
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-42518-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319425184
907			_a.b12955589 _b10-10-17 _c21-11-16
998			_am _a_alco _a_vill _b11-07-17 _cm _dz _ek _feng _ggw _h0
945			_aQA76.9.D343 H834 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11597690 _z06-04-17
999			_c86280 _d86280 _x1