Fuzzy preference queries to relational databases:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
London
Imperial College Press
2012
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| Schlagworte: | |
| Online-Zugang: | FAW01 FAW02 Volltext |
| Beschreibung: | Made available through World Scientific e-Books 1. Introduction. 1.1. Databases and their evolution. 1.2. Preferences and fuzzy sets. 1.3. Overview of the book -- 2. Reminders on relational databases. 2.1. Basic notions and vocabulary. 2.2. Algebraic operations. 2.3. An overview of SQL -- 3. Basic notions on fuzzy sets. 3.1. Introduction. 3.2. Definitions and notations. 3.3. Composition of fuzzy sets. 3.4. Inclusions and implications. 3.5. Fuzzy measures and integrals. 3.6. The extension principle. 3.7. Fuzzy quantified propositions -- 4. Non-fuzzy approaches to preference queries: a brief overview. 4.1. Introduction. 4.2. Quantitative approaches. 4.3. Qualitative approaches. 4.4. Conclusion -- 5. Simple fuzzy queries. 5.1. Introduction. 5.2. An extended relational algebra. 5.3. An overview of a basic version of SQLf. 5.4. Interface for user-defined terms and operators. 5.5. Contextual queries. 5.6. Evaluation of simple fuzzy queries. 5.7. Conclusion -- 6. Fuzzy queries involving quantified statements or aggregates. 6.1. Introduction. 6.2. Quantified statements. 6.3. Aggregates. 6.4. Conclusion -- 7. Division and antidivision of fuzzy relations. 7.1. Introduction. 7.2. Division of fuzzy relations. 7.3. Tolerant division. 7.4. Stratified division. 7.5. Queries mixing division and antidivision. 7.6. Evaluation of division queries. 7.7. Conclusion -- 8. Bipolar fuzzy queries. 8.1. Introduction. 8.2. Preliminaries. 8.3. Extended algebraic operators. 8.4. Implementation aspects. 8.5. Conclusion -- 9. Fuzzy group by. 9.1. Introduction. 9.2. An extended group by clause. 9.3. Having clause. 9.4. Application to association rule mining. 9.5. Evaluation of a fuzzy group by. 9.6. Related work. 9.7. Conclusion -- 10. Empty and plethoric answers. 10.1. Introduction. 10.2. Empty answer problem. 10.3. Plethoric answer problem. 10.4. Conclusion -- 11. Conclusion The manipulation of databases is an integral part of a world which is becoming increasingly and pervasively information-focused. This book puts forward a suggestion to advocate preference queries and fuzzy sets as a central concern in database queries and offers an important contribution to the design of intelligent information systems. It provides a comprehensive study on fuzzy preference queries in the context of relational databases. Preference queries, a recent hot topic in database research, provide a basis for rank-ordering the items retrieved, which is especially valuable for large sets of answers. This book aims to show that fuzzy set theory constitutes a highly expressive framework for modeling preference queries. It presents a study of the algorithmic aspects related to the evaluation of such queries in order to demonstrate that this framework offers a good trade-off between expressivity and efficiency. Numerous examples and proofs are liberally and lucidly demonstrated throughout and greatly enhance the detailed theoretical aspects explored in the book. Researchers working in databases will greatly benefit from this comprehensive and up-to-date study of fuzzy preference queries and it will also become an invaluable reference point for postgraduate students interested in advanced database techniques |
| Beschreibung: | 1 Online-Ressource |
| ISBN: | 1848168691 1848168705 9781848168695 9781848168701 |
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| 500 | |a The manipulation of databases is an integral part of a world which is becoming increasingly and pervasively information-focused. This book puts forward a suggestion to advocate preference queries and fuzzy sets as a central concern in database queries and offers an important contribution to the design of intelligent information systems. It provides a comprehensive study on fuzzy preference queries in the context of relational databases. Preference queries, a recent hot topic in database research, provide a basis for rank-ordering the items retrieved, which is especially valuable for large sets of answers. This book aims to show that fuzzy set theory constitutes a highly expressive framework for modeling preference queries. It presents a study of the algorithmic aspects related to the evaluation of such queries in order to demonstrate that this framework offers a good trade-off between expressivity and efficiency. Numerous examples and proofs are liberally and lucidly demonstrated throughout and greatly enhance the detailed theoretical aspects explored in the book. Researchers working in databases will greatly benefit from this comprehensive and up-to-date study of fuzzy preference queries and it will also become an invaluable reference point for postgraduate students interested in advanced database techniques | ||
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Datensatz im Suchindex
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| isbn | 1848168691 1848168705 9781848168695 9781848168701 |
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| spelling | Pivert, Olivier Verfasser aut Fuzzy preference queries to relational databases Olivier Pivert & Patrick Bosc London Imperial College Press 2012 1 Online-Ressource txt rdacontent c rdamedia cr rdacarrier Made available through World Scientific e-Books 1. Introduction. 1.1. Databases and their evolution. 1.2. Preferences and fuzzy sets. 1.3. Overview of the book -- 2. Reminders on relational databases. 2.1. Basic notions and vocabulary. 2.2. Algebraic operations. 2.3. An overview of SQL -- 3. Basic notions on fuzzy sets. 3.1. Introduction. 3.2. Definitions and notations. 3.3. Composition of fuzzy sets. 3.4. Inclusions and implications. 3.5. Fuzzy measures and integrals. 3.6. The extension principle. 3.7. Fuzzy quantified propositions -- 4. Non-fuzzy approaches to preference queries: a brief overview. 4.1. Introduction. 4.2. Quantitative approaches. 4.3. Qualitative approaches. 4.4. Conclusion -- 5. Simple fuzzy queries. 5.1. Introduction. 5.2. An extended relational algebra. 5.3. An overview of a basic version of SQLf. 5.4. Interface for user-defined terms and operators. 5.5. Contextual queries. 5.6. Evaluation of simple fuzzy queries. 5.7. Conclusion -- 6. Fuzzy queries involving quantified statements or aggregates. 6.1. Introduction. 6.2. Quantified statements. 6.3. Aggregates. 6.4. Conclusion -- 7. Division and antidivision of fuzzy relations. 7.1. Introduction. 7.2. Division of fuzzy relations. 7.3. Tolerant division. 7.4. Stratified division. 7.5. Queries mixing division and antidivision. 7.6. Evaluation of division queries. 7.7. Conclusion -- 8. Bipolar fuzzy queries. 8.1. Introduction. 8.2. Preliminaries. 8.3. Extended algebraic operators. 8.4. Implementation aspects. 8.5. Conclusion -- 9. Fuzzy group by. 9.1. Introduction. 9.2. An extended group by clause. 9.3. Having clause. 9.4. Application to association rule mining. 9.5. Evaluation of a fuzzy group by. 9.6. Related work. 9.7. Conclusion -- 10. Empty and plethoric answers. 10.1. Introduction. 10.2. Empty answer problem. 10.3. Plethoric answer problem. 10.4. Conclusion -- 11. Conclusion The manipulation of databases is an integral part of a world which is becoming increasingly and pervasively information-focused. This book puts forward a suggestion to advocate preference queries and fuzzy sets as a central concern in database queries and offers an important contribution to the design of intelligent information systems. It provides a comprehensive study on fuzzy preference queries in the context of relational databases. Preference queries, a recent hot topic in database research, provide a basis for rank-ordering the items retrieved, which is especially valuable for large sets of answers. This book aims to show that fuzzy set theory constitutes a highly expressive framework for modeling preference queries. It presents a study of the algorithmic aspects related to the evaluation of such queries in order to demonstrate that this framework offers a good trade-off between expressivity and efficiency. Numerous examples and proofs are liberally and lucidly demonstrated throughout and greatly enhance the detailed theoretical aspects explored in the book. Researchers working in databases will greatly benefit from this comprehensive and up-to-date study of fuzzy preference queries and it will also become an invaluable reference point for postgraduate students interested in advanced database techniques COMPUTERS / Expert Systems bisacsh Expert systems (Computer science) fast Uncertainty (Information theory) fast Expert systems (Computer science) Uncertainty (Information theory) Fuzzy-Menge (DE-588)4061868-7 gnd rswk-swf Relationale Datenbank (DE-588)4049358-1 gnd rswk-swf Relationale Datenbank (DE-588)4049358-1 s Fuzzy-Menge (DE-588)4061868-7 s 1\p DE-604 Bosc, Patrick Sonstige oth http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=457190 Aggregator Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Pivert, Olivier Fuzzy preference queries to relational databases COMPUTERS / Expert Systems bisacsh Expert systems (Computer science) fast Uncertainty (Information theory) fast Expert systems (Computer science) Uncertainty (Information theory) Fuzzy-Menge (DE-588)4061868-7 gnd Relationale Datenbank (DE-588)4049358-1 gnd |
| subject_GND | (DE-588)4061868-7 (DE-588)4049358-1 |
| title | Fuzzy preference queries to relational databases |
| title_auth | Fuzzy preference queries to relational databases |
| title_exact_search | Fuzzy preference queries to relational databases |
| title_full | Fuzzy preference queries to relational databases Olivier Pivert & Patrick Bosc |
| title_fullStr | Fuzzy preference queries to relational databases Olivier Pivert & Patrick Bosc |
| title_full_unstemmed | Fuzzy preference queries to relational databases Olivier Pivert & Patrick Bosc |
| title_short | Fuzzy preference queries to relational databases |
| title_sort | fuzzy preference queries to relational databases |
| topic | COMPUTERS / Expert Systems bisacsh Expert systems (Computer science) fast Uncertainty (Information theory) fast Expert systems (Computer science) Uncertainty (Information theory) Fuzzy-Menge (DE-588)4061868-7 gnd Relationale Datenbank (DE-588)4049358-1 gnd |
| topic_facet | COMPUTERS / Expert Systems Expert systems (Computer science) Uncertainty (Information theory) Fuzzy-Menge Relationale Datenbank |
| url | http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=457190 |
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