Fuzzy databases: Modeling, design and implementation

Fuzzy Databases: Modeling, Design and Implementation

Volumn , Issue , 2005, Pages 1-320

  Galindo, José ^a   Urrutia, Angélica ^b   Piattini, Mario ^c  

^a UNIVERSITY OF MÁLAGA   (Spain)

^b UNIVERSIDAD CATÓLICA DEL MAULE   (Chile)

^c UNIVERSITY OF CASTILLA LA MANCHA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84898357532     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-59140-324-1     Document Type: Book

References (207)

1. American National Standard Institute. (1986). American national standard for information systems: Database language SQL. FDT, ANSI X3, 135-1986. New York: American National Standard Institute.
2. American National Standard Institute. (1992). Database language SQL. ANXI X3, 135-1992. New York: American National Standard Institute.
3. Andreasen, T., & Pivert, O. (1994). On the weakening of fuzzy relational queries. In Proceedings of the Eighth International Symposium on Methodologies for Intelligent Systems (pp. 144-153).
4. Andreasen, T., & Pivert, O. (1995). Improving answers to failing fuzzy relational queries. In Proceedings of the Sixth International Fuzzy Systems Association Congress (pp. 414-418).
5. Aranda, M. C., Galindo, J., & Urrutia, A. (2002). Museos digitales en Internet: Modelo EER difuso y recuperación de imágenes basada en contenido. In IV Turismo y Tecnologías de la Información y las Comunicaciones (TuriTec'2002), Málaga, Spain (pp. 411-425).
6. Aranda, M. C., & Galindo, J. (1998). Clasificación de imágenes de una base de datos utilizando información de su forma. IV Jornadas Internacionales de Informática (pp. 565-574). Spain: Las Palmas de Gran Canaria.
7. Atzeni, P., Ceri, S., Paraboschi, S., & Torlone, R. (1999). Database systems concepts languages and architecture. McGraw-Hill.
8. Baldwin, J. (1983). Knowledge engineering using a fuzzy relational inference language. In Proceedings of the IFAC Symposium on Fuzzy Information Knowledge Representation and Decision Analysis (pp. 15-21).
9. Barbara, D., Garcia-Molina, H., & Porter, D. (1992). The management of probabilistic data. IEEE Transactions on Knowledge and Data Engineering, 4, 487-502.
10. Barranco, C. D., Campaña, J., Cubero, J. C., & Medina, J. M. (2005). A fuzzy object relational approach to flexible real estate trade. In Proceedings of the Fourth WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Databases.
11. Barranco, C. D., Campaña, J., Medina, J. M., & Pons, O. (2004). ImmoSoftWeb: A Web-based fuzzy application for real estate management. In Lecture Notes in Computer Science (Vol. 3034): Advances in Web intelligence: Second international atlantic Web intelligence conference (pp. 196-206). Heidelberg: Springer-Verlag.
12. Batini, C., Ceri, S., & Navathe, S. (1994). Diseño conceptual de bases de datos. Addison Wesley/Díaz de Santos.
13. Bezdek, J. (1981). Pattern recognition with fuzzy objective function algorithms. New York: Plenum Press.
14. Blanco, I. (2001). Deducción en bases de datos relacionales difusas. Unpublished doctoral dissertation, University of Granada, Spain. Retrieved from http://frontdb.ugr.es
15. Blanco, I., Cubero, J. C., Cuenca, F., & Pons, O. (1999). Implementation of an inference engine for fuzzy databases. In Proceedings of the EUSFLAT-ESTYLF Joint Conference, Palma de Malloraca, Spain (pp. 493-496).
16. Blanco, I., Cubero, J. C., Pons, O., & Vila, M. A. (2000). An implementation for fuzzy deductive relational databases. In G. Bordogna & G. Pasi (Eds.), Recent issues on fuzzy databases (pp. 183-207). Physica-Verlag.
17. Bordogna, G., Leporati, A., Lucarella, D., & Pasi G. (2000). The fuzzy object-oriented database management system. In G. Bordogna & G. Pasi (Eds.), Recent issues on fuzzy databases (pp. 209-236). Physica-Verlag.
18. Bordogna, G., Lucarella, D., & Pasi, G. (1999). A fuzzy object-oriented data model managing vague and uncertain information. International Journal of Intelligent Systems, 14(7), 623-651.
19. Bosc, P. (1998). On diverse answers issued from flexible queries. In T. Andreasen, H. Christiansen, & H. L. Larsen (Eds.), Lecture Notes in Artificial Intelligence (Vol. 1495): Flexible query answering systems (pp. 55-67). Springer.
20. Bosc, P., Dubois, D., Pivert, O., & Prade, H. (1997). Flexible queries in relational databases: The example of the division operador. Theoretical Computer Science, 171, 281-302.
21. Bosc, P., Galibourg, M., & Hamon, G. (1988). Fuzzy querying with SQL: Extensions and implementation aspects. Fuzzy Sets and Systems, 28, 333-349.
22. Bosc, P., & Galibourg, M. (1989). Indexing principles for a fuzzy data base. Information Systems, 14(6), 493-499.
23. Bosc, P., & Pivert, O. (1995). SQLf: A relational database language for fuzzy querying. IEEE Transactions on Fuzzy Systems, 3, 1-17.
24. Buckles, B. P., & Petry, F. E. (1982a). Fuzzy databases and their applications. In M. Gupta & E. Sanchez (Eds.), Fuzzy information and decision processes (Vol. 2, pp. 361-371). North-Holland, Amsterdam.
25. Buckles, B. P., & Petry, F. E. (1982b). A fuzzy representation of data for relational databases. Fuzzy Sets Systems, 7, 213-226.
26. Buckles, B. P., & Petry, F. E. (1984). Extending the fuzzy database with fuzzy numbers. Information Sciences, 34, 45-55.
27. Buckles, B. P., & Petry, F. E. (1985). Uncertainty models in information and database systems. Information Sciences, 11, 77-87.
28. Butnario, D., & Klement, E. P. (1993). Triangular norm-based measures and games with fuzzy coalitions. Dordrecht: Kluwer Academic.
29. Carrasco, R., Galindo, J., Aranda, M. C., Medina, J. M., & Vila, A. (1998). Classification in databases using a fuzzy query language. In C. S. R. Prabhu (Ed.), Databases for the millennium 2000 (pp. 193-203). McGraw-Hill.
30. Carrasco, R., Galindo, J., & Vila, A. (2001). Using artificial neural network to define fuzzy comparators in FSQL with the criterion of some decision-maker. In J. Mira & A. Prieto (Eds.), Lecture Notes in Computer Science (Vol. 2085, Part II): Bio-inspired Applications of Connectionism (pp. 587-594). Springer.
31. Carrasco, R., Galindo, J., Vila, A., & Medina, J. M. (1999). Clustering and fuzzy classification in a financial data mining environment. In Proceedings of the Third International ICSC Symposium on Soft Computing (SOCO'99), Italy (pp. 713-720).
32. Carrasco, R., Vila, A., Galindo, J., & Cubero, J. C. (2000a). FSQL: A tool for obtaining fuzzy dependencies. In Proceedings of the Eighth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Spain (pp. 1916-1919).
33. Carrasco, R., Vila, A., Galindo, J., & Cubero, J. C. (2000b). Fuzzy global dependencies in databases. In X Congreso Español sobre Tecnologías y Lógica Fuzzy, Spain (pp. 175-180).
34. Carrasco, R. A. (2003). Lenguajes e interfaces de alto nivel para data mining con aplicación práctica a entornos financieros. Unpublished doctoral dissertation, University of Granada, Spain.
35. Carrasco, R. A., Vila, M. A., & Galindo, J. (2002). FSQL: A flexible query language for data mining. In M. Piattini, J. Filipe, & J. Braz (Eds.), Enterprise information systems IV (pp. 68-74). Kluwer Academic.
36. Cavallo, R., & Pittarelli, M. (1987). The theory of probabilistic databases. In Proceedings of the Third International Conference on Very Large Databases (pp. 102-110).
37. Chamberlin, D. D., & Boyce, R. F. (1974). SEQUEL: A structured English query language. In Proceedings of the ACM SIGMOD Workshop on Data Description (pp. 249-264).
38. Chamberlin, D. D. et al. (1976). SEQUEL 2: A unified approach to data definition, manipulation and control. IBM Journal of Research and Development, 20(6), 560-575.
39. Chaudhry, N., Moyne, J., & Rundensteiner, E. (1994). A design methodology for databases with uncertain data. In Proceedings of the Seventh International Working Conference on Scientific and Statistical Database Management, Charlottesville, VA (pp. 32-41). Retrieved from www.mitexsolutions.com
40. Chaudhry, N., Moyne, J., & Rundensteiner, E. A. (1999). An extended database design methodology for uncertain data management. Information Sciences, 121, 83-112.
41. Chen, G., Kerre, E., & Vandenbulcke, J. (1994). A computational algorithm for the ffd transitivity closure and complete axiomatization of fuzzy functional dependence (ffd). International Journal of Intelligent Systems, 9, 421-440.
42. Chen, G. Q. (1998). Fuzzy logic in data modeling, semantics constraints, and databases design. In A. K. Elmagarmid (Ed.), The Kluwer international series on advances in database systems.
43. Chen, G. Q., & Kerre, E. E. (1998). Extending ER/EER concepts towards fuzzy conceptual data modeling. In Proceedings of the IEEE International Conference on Fuzzy Systems, 2 (pp. 1320-1325).
44. Chen, M., Han, J., & Yu, P. S. (1996). Data mining: An overview from a data base perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6), 866-883.
45. Chen, P. (1976). The entity-relationship model toward a unified view of data. ACM Transactions on Database Systems, 1(1), 9-36.
46. Codd, E. F. (1979). Extending the database relational model to capture more meaning. ACM Transactions on Database Systems, 4, 262-296.
47. Codd, E. F. (1986). Missing information (applicable and inapplicable) in relational databases. ACM SIGMOD Record, 15(4).
48. Codd, E. F. (1987). More commentary on missing information in relational databases. ACM SIGMOD Record, 16(1).
49. Codd, E. F. (1990). The relational model for database management, Version 2. Reading, MA: Addison-Wesley.
50. Connolly, T., Begg, C., & Strachan, A. (1998). Databases systems, a practical pproach to design, implementation and management (2nd ed.). Addison-Wesley.
51. Cubero, J. C., Medina, J. M., Pons, O., & Vila, M. (1998). Fuzzy loss decompositions in databases. Fuzzy Sets and Systems, 97(2), 145-167.
52. Cubero, J. C., & Vila, M. A. (1994). A new definition of fuzzy functional dependency in fuzzy relational databases. International Journal of Intelligent Systems, 9, 441-449.
53. Date, C. J. (1986). Null values in database management. In C. J. Date (Ed.), Relational Database: Selected Writings. Reading, MA: Addison-Wesley.
54. Date, C. J., & Darwen, H. (1997). A guide to SQL standard (4th ed.). Addison-Wesley.
55. Davis, J. P., & Bonnell, R. D. (1989). Modeling semantic constraints with logic in the EARL data model. In Proceedings of the Fifth International Conference on Data Engineering (pp. 226-233).
56. De Caluwe, R. (Ed.) (1997). Fuzzy and uncertain object-oriented databases: Concepts and models. Advances in Fuzzy Systems: Application and Theory, 13.
57. De Luca, A., & Termini, S. (1974). Entropy of L-fuzzy sets. Information and Control, 24, 55-73.
58. De Miguel, A., Piattini, M., & Marcos, E. (1999). Diseño de bases de datos relacionales. Spain: Ra-Ma Editorial.
59. De Miguel, A., & Piattini, M. (1999). Fundamentos y modelos de bases de datos relacionales (2nd ed.). Spain: Ra-Ma Editorial.
60. Delgado, M., Gómez-Skarmeta, A. F., & Vila, A. (1996). On the use of hierarchical clustering in fuzzy modelling. International Journal of Approximate Reasoning, 14, 237-257.
61. Delgado, M., Sánchez, D., & Vila, M. A. (1999). A survey of methods for evaluating quantified sentences. In Proceedings of the EUSFLAT-ESTYLF Joint Conference, Spain (pp. 279-282).
62. Delgado, M., Sánchez, D., & Vila, M. A. (2000). Fuzzy cardinality based evaluation of quantified sentences. International Journal of Approximate Reasoning, 23, 23-66.
63. Delgado, M., Verdegay, J. L., & Vila, M. A. (1993). On aggregation operations of linguistic labels. International Journal of Intelligent Systems, 8, 351-370.
64. Di Battista, G., & Lenzerini, M. (1993). Deductive entity relationship modeling. IEEE Transactions on Knowledge and Data Engineering, pp. 439-450.
65. Dubois, D., & Prade, H. (1980). Fuzzy sets and systems: Theory and applications. New York: Academic Press.
66. Dubois, D., & Prade, H. (1985a). Fuzzy cardinality and the modeling of imprecise quantification. Fuzzy Sets and Systems, 16, 190-230.
67. Dubois, D., & Prade, H. (1985b). Fuzzy number. An overview, the analysis of fuzzy information. Boca Raton, FL: CRS Press.
68. Dubois, D., & Prade, H. (1988). Possibility theory. An approach to computerized processing of uncertainty. New York: Plenum Press.
69. Dubois, D., & Prade, H. (1989). Processing fuzzy temporal knowledge. IEEE Transactions Systems Man Cybernetics, 19, 729-744.
70. Dubois, D., & Prade, H. (1992). Gradual rules in approximate reasoning. Information Sciences, 61, 103-122.
71. Dubois, D., & Prade, H. (1998). Théorie des possibilites applications á la représentation des connaissances en informatique (2nd ed.).
72. Elmasri, R., & Navathe, S. B. (1997). Sistemas de bases de datos conceptos fundamentales (2nd ed.). Addison-Wesley.
73. Elmasri, R., & Navathe, S. B. (2000). Fundamentals of database systems (3rd ed.). Addison-Wesley.
74. Elmasri, R., Weeldreyer, J., & Hevner, A. (1985, May). The category concept: An extension to the entity-relationship model. International Journal on Data and Knowledge Engineering, 1(1).
75. Eloy-García, J. (2003). Recuperación de imágenes usando atributos difusos. Proyecto Fin de Carrera de Ingeniería Superior en Informática (Universidad de Málaga), directed by M.C. Aranda and J. Galindo. Retrieved from http://www.lcc.uma.es/~ppgg/PFC
76. Escobar, C. (2003). Software para control difuso de todo tipo de sistemas (SCD): Aplicación al Control de Invernaderos Industriales. Proyecto Fin de Carrera de Ingeniería Técnica Industrial en Electrónica (Universidad de Málaga), directed by J. Galindo. Retrieved from http://www.lcc.uma.es/~ppgg/PFC
77. Fowler, M., & Scott, K. (1999). UML gota a gota. Addison Wesley Longman, Pearson.
78. Frawley, W. J., Piatetsky-Shapiro, G., & Matheus, C. J. (1991). Knowledge discovery in databases: An overview. In G. Piatetsky-Shapiro & W. J. Frawley (Eds.), Knowledge discovery in databases (pp. 1-31). The AAAI Press.
79. Fuhr, N. (1990). A probabilistic framework for vague queries and imprecise information in databases. In Proceedings of the Sixth International Conference on Very Large Data Bases (pp. 77-85).
80. Fujishiro, I. et al. (1991). The design of a graph-oriented schema for the management of individualized fuzzy data. Japanese Journal of Fuzzy Theory and Systems, 3(1), 1-14.
81. Fukami, S., Umano, M., Muzimoto, M., & Tanaka, H. (1979). Fuzzy database retrieval and manipulation language (Tech. rep. no. AL-78-85). IEICE Technical Reports, 78(233), 65-72.
82. Galindo, J. (1999). Tratamiento de la imprecisión en bases de datos relacionales: Extensión del modelo y adaptación de los SGBD actuales. Unpublished doctoral dissertation, University of Granada, Spain.
83. Galindo, J. (2001). Curso sobre "Conjuntos y Sistemas Difusos (Lógica Difusa y Aplicaciones)." Informe Técnico de Docencia. LCC-ITI-2001-11, Universidad de Málaga, Departamento de Lenguajes y Ciencias de la Computación. Retrieved from http://www.lcc.uma.es
84. Galindo, J. (2005). New characteristics in FSQL, a fuzzy SQL for fuzzy databases. In Proceedings of the Fourth WSEAS International Conference on Artificial Intelligence, Knowledge Engineering, and Databases. Retrieved from www.lcc.uma.es/~ppgg/FSS
85. Galindo, J., & Aranda, M. C. (1999). Gestión de una agencia de viajes usando bases de datos difusas y FSQL. In Proceedings of Turismo y Tecnologías de la Información y Las Comunicaciones: Nuevas Tecnologías y Calidad (TuriTec '99), Malaga, Spain (pp. 343-6).
86. Galindo, J., Aranda, M. C., Guevara, A., Caro, J. L., & Aguayo, A. (2002). Applying fuzzy databases and FSQL to the management of rural accommodation. Tourist Management Journal, 23(6), 623-629.
87. Galindo, J., Medina, J. M., Aranda, M. C. (1999). Querying fuzzy relational databases through fuzzy domain calculus. International Journal of Intelligent Systems, 14(4), 375-411.
88. Galindo, J., Medina, J. M., Cubero, J. C., & García, M. T. (2001). Relaxing the universal quantifier of the division in fuzzy relational databases. International Journal of Intelligent Systems, 16(6), 713-742.
89. Galindo, J., Medina, J. M., Cubero, J. C., & Pons, O. (1999). Management of an estate agency allowing fuzzy data and flexible queries. In Proceedings of the EUSFLAT-ESTYLF Joint Conference, Spain (pp. 485-488).
90. Galindo, J., Medina, J. M., & Rodríguez, J. M. (2000). Comparadores para bases de datos difusas: Definiciones, clases y relaciones. In X Congreso Español sobre Tecnologías y Lógica Fuzzy, Spain, (pp. 187-192). Retrieved from www.lcc.uma.es
91. Galindo, J., Medina, J. M., Vila, M. A., & Pons, O. (1998). Fuzzy comparators for flexible queries to databases. In Proceedings of the Sixth Iberoamerican Conference on Artificial Intelligence, (pp. 29-41).
92. Galindo, J., Medina, M., Pons, O., & Cubero, J. C. (1998). A server for fuzzy SQL queries. In T. Andreasen, H. Christiansen, & H. L. Larsen (Eds.), Lecture Notes in Artificial Intelligence (Vol. 1495): Flexible query answering systems (pp. 164-174). Springer.
93. Galindo, J., Oliva, R. F., & Carrasco, R. A. (2004). Acceso Web a bases de datos difusas: Un cliente visual de fuzzy SQL. In XII Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF 2004), Jaen, Spain (pp. 83-88).
94. Galindo, J., Urrutia, A., & Carrasco, R. (2002). Cuatro tipos de restricciones difusas en especializaciones. In Workshop de Bases de Datos, Chile (pp. 79-88).
95. Galindo, J., Urrutia, A., Carrasco, R., & Piattini, M. (2001). Fuzzy constraints using the enhanced entity-relationship model. In Proceedings of the 21st International Conference of the Chilean Computer Science Society, Chile (pp. 86-94).Retrieved from http://computer.org/proceedings/sccc/1396/13960086abs.htm
96. Galindo, J., Urrutia, A., Carrasco, R., & Piattini, M. (2004). Relaxing constraints in enhanced entity-relationship models using fuzzy quantifiers. IEEE Transactions on Fuzzy Systems, 12(6), 780-796.
97. Galindo, J., Urrutia, A., & Piattini, M. (2004a). Fuzzy aggregations and fuzzy specializations in fuzzy EER model. In Keng Siau (Ed.), Advanced Topics in Database Research (Vol. III, pp. 105-126). Hershey, PA: Idea Group Publishing.
98. Galindo, J., Urrutia, A., & Piattini, M. (2004b). Storing fuzzy knowledge and fuzzy metaknowledge in relational systems. In Proceedings of the Fifth WSEAS International Conference on Fuzzy Sets and Fuzzy Systems, Italy.
99. Geneste, L., & Ruet, M. (2001). Experience based configuration. In Proceedings of the 17th International Conference on Artificial Intelligence, USA (pp. 51-60).
100. Geneste, L., & Ruet, M. (2002). Fuzzy case based configuration. In Proceedings of the 15th European Conference on Artificial Intelligence, France (pp. 130-142).
101. George, R., Srikanth, R., Petry, F. E., & Buckles, B. P. (1996). Uncertainty management issues in the object-oriented data model. IEEE Transactions on Fuzzy Systems, 4(2), 179-192.
102. Giardina, C. (1979). Fuzzy databases and fuzzy relational associative processors. Hoboken, NJ: Stevens Institute of Technology.
103. Goncalves, M., & Tineo, L. (2001a). SQLf flexible querying extension by means of the norm SQL2. In Proceedings of the IEEE International Fuzzy Systems Conference (pp. 473-476).
104. Goncalves, M., & Tineo, L. (2001b). SQLf3: An extension of SQLf with SQL3 features. In Proceedings of the IEEE International Fuzzy Systems Conference, (pp. 477-480).
105. Goswami, A., & Kumar, P. (2001). Conceptual modeling of fuzzy object-oriented database systems. In ICEIS, 287-291.
106. Grant, J. (1980). Incomplete information in a relational database. Fundamenta Informaticae, 3, 363-378.
107. Gregersen, H., & Jensen, C. S. (1999). Temporal entity-relationship models: A survey. IEEE Transactions on Knowledge and Data Engineering, 464-497.
108. Hammer, M., & McLeod, D. (1981). Database description with SDM: A semantic data model. ACM Transactions on Database Systems, 6(3), 351-386.
109. Hernández, R., Fernández, C., & Baptista, P. (1998). Metodología de la investigación. McGraw Hill.
110. Jain, A. K. (1989). Fundamentals of digital image processing. Englewood Cliffs, NJ: Prentice-Hall.
111. Jensen, C. et al. (1994). A glossary of temporal database concepts. ACM SIGMOD Record, 23(1).
112. Kerre, E. E, & Chen, G. (1995). An overview of fuzzy data models. In P. Bosc & J. Kacprzyk (Eds.), Studies in fuzziness: Fuzziness in database management systems (pp. 23-41). Physica-Verlag.
113. Kerre, E. E., & Chen, G. (2000). Fuzzy data modeling at a conceptual level: Extending ER/EER concepts. In O. Pons, M. A. Vila, & J. Kacprzyk (Eds.), Knowledge management in fuzzy databases (pp. 3-11). Physica-Verlag.
114. Kosko, B. (1992). Neural networks and fuzzy systems: A dynamical systems approach to machine intelligence. Englewood Cliffs, NJ: Prentice-Hall.
115. Kruse, R., Gebhardt, J., & Klawonn, F. (1994). Foundations of fuzzy systems. Wiley.
116. Lipski, W. (1979). On semantic issues connected with incomplete information databases. ACM Transactions on Database Systems, 4, 262-296).
117. Liu, Y., & Kerre, E. E. (1998a). An overview of fuzzy quantifiers. Part I: Interpretations. Fuzzy Sets and Systems, 95(1), 1-21.
118. Liu, Y., & Kerre, E. E. (1998b). An overview of fuzzy quantifiers. Part II: Reasoning and applications. Fuzzy Sets and Systems, 95(2), 135-146.
119. Luque, I., Gómez-Nieto, M. A., López, E., & Cerruela, G. (2001). Bases de datos desde chen hasta codd con ORACE. Editorial Rama.
120. Ma, Z. M., Zhang, W. J., & Ma, W. Y. (2004). Extending object-oriented databases for fuzzy information modeling. Information Systems, 29, 421-435.
121. Ma, Z. M., Zhang, W. J., Ma, W. Y., & Chen, Q. (2001). Conceptual design of fuzzy object-oriented databases using extended entity-relationship model. International Journal of Intelligent Systems, 16(6), 697-711.
122. Malik, J., & Binford, T. O. (1983). Reasoning in time and space. In Proceedings of the Eighth International Joint Conference on Artificial Intelligence (pp. 343-345).
123. Marín, N., Pons, O., & Vila, M. A. (2000). Fuzzy types: A new concept of type for managing vague structures. International Journal of Intelligent Systems, 15, 1061-1085.
124. Marín, N., Vila, A., Blanco, I., & Pons, O. (2000). Fuzzy types as a new layer on an object oriented database system. In Proceedings of the Eighth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU'2000), Madrid, Spain (pp. 1099-1102).
125. McNeill, F. M., & Thro, E. (1994). Fuzzy logic: A practical approach. AP Professional.
126. Medina, J. M. (1994). Bases de datos relacionales difusas: Modelo teórico y aspectos de su implementación. Unpublished doctoral dissertation, Universidad de Granada, Spain. Retrieved from decsai.ugr.es
127. Medina, J. M., Pons, O., & Vila, M. A. (1994). GEFRED: A generalized model of fuzzy relational databases. Information Sciences, 76(1/2), 87-109.
128. Medina, J. M, Pons, O., & Vila, A. (1995). FIRST: A fuzzy interface for relational systems. Proceedings of the Sixth International Fuzzy Systems Association World Congress, Brazil (pp. 409-412).
129. Menger, K. (1942). Statistical metric spaces. In Proceedings of the National Academy of Sciences, 37, USA (pp. 535-537).
130. Michalski, R. S., & Stepp, R. E. (1984). Learning from observation: Conceptual clustering. In R. Michalski, J. Carbonell, & T. Mitchell (Eds.), Machine learning: An artificial intelligence approach (pp. 331-363). Berlin: Springer-Verlag.
131. Mohammad, J., Vadiee, N., & Ross, T. J. (Eds.) (1993). Fuzzy logic and control: Software and hardware applications. Englewood Cliffs, NJ: Prentice-Hall.
132. Mokhtarian, F., & Mackworth, A. (1986). Scale-based description and recognition of planar curves and two-dimensional shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8, 34-43.
133. Morrone, M. C., & Owens, R. A. (1987). Feature detection from local energy. Pattern Recognition Letters, 6, 303-313.
134. Motro, A. (1990). Accommodating imprecision in database systems: Issues and solutions. SIGMOD Record, 19(4), 69-74.
135. Motro, A. (1995). Management of uncertainty in database system. In W. Kim (Ed.), Modern database system: The object model, interoperability and beyond. Addison-Wesley.
136. Mouaddib, N. (1994). Fuzzy identification database: The nuanced relation division. International Journal of Intelligent Systems, 9, 461-473.
137. Nakajima, H., Sogoh, T., & Arao, M. (1993). Fuzzy database language and library: Fuzzy extension to SQL. In Proceedings of the Second International Conference on Fuzzy Systems (FUZZ-IEEE'93) (pp. 477-482).
138. Oliva, R. F. (2003). Visual FSQL: Gestión visual de bases de datos difusas en ORACLE a través de Internet usando FSQL. Proyecto Fin de Carrera, directed by J. Galindo, of Ingeniería Superior en Informática, in the University of Málaga. Retrieved from http://www.lcc.uma.es/~ppgg/PFC
139. Ozawa, J., & Yamada, K. (1994). Cooperative answering with macro expression of a database. In Proceedings of the International Conference on Information Processing and Management in Knowledge-Based Systems (pp. 17-22).
140. Patrick, J. J. (2002). SQL fundamentals (2nd ed.). Prentice-Hall.
141. Pawlak, Z. (1982). Rough sets. International Journal of Computer and Information Sciences, 11, 341-356.
142. Pawlak, Z. (1991). Rough sets: Theoretical aspects of reasoning about data. Norwell, MA: Kluwer Academic.
143. Pedrycz, W., & Gomide, F. (1998). An introduction to fuzzy sets: Analysis and design. Cambridge, MA: MIT Press.
144. Petry, F. E. (with Bose, P.). (1996). Fuzzy databases: Principles and applications. In H. J. Zimmermann (Ed.), International series in intelligent technologies. Kluwer Academic.
145. Piegat, A. (2001). Fuzzy modeling and control. Physica-Verlag.
146. Pons, O. (1996). Representación lógica de bases de datos difusas: Fundamento teórico e implementación. Unpublished doctoral dissertation, Universidad de Granada.
147. Prade, H. (1984). Lipski's approach to incomplete information databases restated and generalized in the setting of Zadeh's possibitity theory. Information Systems, 9, 27-42.
148. Prade, H., & Testemale, C. (1984). Generalizing database relational algebra for the treatment of incomplete/uncertain information and vague queries. Information Sciences, 34, 115-143.
149. Prade, H., & Testemale, C. (1987a). Fuzzy relational databases: Representational issues and reduction using similarity measures. J. Am. Soc. Information Sciences, 38(2), 118-126.
150. Prade, H., & Testemale, C. (1987b). Representation of soft constraints and fuzzy attribute values by means of possibility distributions in databases. In J. Bezdek (Ed.), Analysis of fuzzy information (Vol. II): Artificial intelligence and decision systems (pp. 213-229). CRC Press.
151. Quian Da-qun. (1992). Representation and use of imprecise temporal knowledge in dynamic systems. Fuzzy Sets and Systems, 50, 59-77.
152. Raju, K., & Majumdar, A. (1988). Fuzzy functional dependencies and lossless joint decomposition of fuzzy relational database system. ACM Transactions on Database Systems, 13(2), 129-166.
153. Reingruber, M., & Gregory, W. (1994). The data modeling handbook: A best-practice approach to building quality data models. Wiley.
154. Rumbaugh, J., Jacobson, J., & Booch, G. (1999). The unified modeling language reference manual. Addison-Wesley.
155. Rundensteiner, E., & Bic, L. (1989). Semantic database models and their potential for capturing imprecision. In Proceedings of the Conference on Management of Data, India, (pp. 43-57).
156. Ruspini, E. (1986). Imprecision and uncertainty in the entity-relationship model. In H. Prade & C. V. Negoita, Fuzzy logic in knowledge engineering (pp. 18-22). Verlag TUV Rheinland.
157. Sánchez, D. (1999). Adquisición de relaciones entre atributos en bases de datos relacionales. Unpublished doctoral dissertation, University of Granada, Spain.
158. Saaty, T. L. (1980). The analytic hierarchy processes. New York: McGraw- Hill.
159. Schweizer, B., & Sklar, A. (1983). Probabilistic metric spaces. North- Holland, Amsterdam.
160. Shoshani, A., & Wong, H. (1985). Statistical and scientific database issues. IEEE Transactions on Software Engineering, 11, 235-256.
161. Silverschatz, A., Korth, H. F., & Sudarshan, S. (2002). Database system concepts. New York: McGraw-Hill.
162. Snodgrass, R. (1995). The TSQL2 temporal query language. Kluwer.
163. Tahani, V. (1977). A conceptual framework for fuzzy query processing a set toward very intelligent database system. Information Processing and Management, 13, 289-303.
164. Tré, G., de Caluwe, R., & Van der Cruyssen, B. (2000). A generalised object-oriented database model. In G. Bordogna & G. Pasi (Eds.), Recent issues on fuzzy databases (pp. 155-182). Physica-Verlag.
165. Trillas, E. (1979). Sobre funciones de negación en la teoría de conjuntos difusos. Stochastica, 3(1), 47-59.
166. Umano, M. (1982). Freedom-O: A fuzzy database system. In M. Gupta & E. Sanchez (Eds.), Fuzzy information and decision processes (pp. 339-347). North-Holland, Amsterdam.
167. Umano, M. (1983). Retrieval from fuzzy database by fuzzy relational algebra. In M. Gupta & E. Sanchez (Eds.), Fuzzy information, knowledge representation and decision analysis (pp. 1-6). New York: Pergamon Press.
168. Umano, M., & Fukami, S. (1994). Fuzzy relational algebra for possibility-distribution-fuzzy-relation model of fuzzy data. Journal of Intelligent Information Systems, 3, 7-28.
169. Urrutia, A. (2002). Implementación de bases de datos difusas: Un caso de control de la calidad del papel. Revista electrónica Gerencia Tecnología Informática AEDO, Volumen 1, número 1. Colombia. Retrieved from http://cidlisuis.org
170. Urrutia, A. (2003). Definición de un modelo conceptual para bases de datos difusas. Unpublished doctoral dissertation, University of Castilla-La Mancha, Spain. Retrieved from http://www.ganimides.ucm.cl/aurrutia
171. Urrutia, A., & Galindo, J. (2001, December). Notación para datos con imprecisión en un modelo conceptual difuso. Revista Académica de la Universidad Católica del Maule (number 27, pp. 39-48). Chile.
172. Urrutia, A., & Galindo, J. (2002). Algunos aspectos del modelo conceptual EER difuso: Aplicación al caso de una agencia inmobiliaria. In XI Congreso Español sobre Tecnologías y Lógica Fuzzy, Spain (pp. 359-364).
173. Urrutia, A., Galindo, J., & Jiménez, L. (2001). Representación de información imprecisa en un modelo conceptual EER difuso. In VIII Congreso Internacional de Investigación en Ciencias Computacionales, México (pp. 15-27).
174. Urrutia, A., Galindo, J., & Jiménez, L. (2002). Extensión del modelo conceptual EER para representar tipos de datos difusos. I+D Computación, 1(2). Retrieved from http://www.sd-cenidet.com.mx/Revista
175. Urrutia, A., Galindo, J., & Piattini, M. (2002). Modeling data using fuzzy attributes. In Proceedings of the 22nd International Conference of the Chilean Computer Science Society, Chile (pp. 117-123).
176. Urrutia, A., & Piattini, M. (2001). Transformation of imprecise data to linguistic labels for ERModels. In Proceedings of the Seventh International Conference on Information Systems Analysis and Synthesis, USA (pp. 351-355).
177. Van Gyseghem, N., & de Caluwe, R. (1997). The UFO model: Dealing with imperfect information. In Fuzzy and uncertain object-oriented data-bases, concepts and models (pp. 123-185). World Scientific.
178. Van Gyseghem, N., de Caluwe, R., & Vandenberghe, R. (1993, March). UFO: Uncertainty and fuzziness in an object-oriented model. In Proceedings of the Second IEEE International Conference Fuzzy Systems, San Francisco (pp. 773-778).
179. Vandenberghe, R. M. (1991). An extended entity-relationship model for fuzzy databases based on fuzzy truth values. In Proceedings of the Fourth International Fuzzy Systems Association World Congress, Brussels (pp. 280-283).
180. Varas, M., Contreras, R., & Campos, D. (1998). Constraints in generalization structures in conceptual database schemes. In Conferencia Internacional de la Sociedad Chilena de Ciencia de la Computación, Chile.
181. Vert, G., Morris, A., Stock, M., & Jankowski, P. (2000). Extending entity-relationship modelling notation to manage fuzzy datasets. In Proceedings of the Eighth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Spain (pp. 1131-1138).
182. Vila, M., Cubero, J. C., Pons, O., & Medina, J. M. (n.d.). A fuzzy object-oriented data model represented by means of a semantic data model. Granada, Spain: Universidad de Granada, Department of Computer Science.
183. Virant, J., & Zimic, N. (1996). Attention to time in fuzzy logic. Fuzzy Sets and Systems, 82(1), 39-49.
184. Weber, R. (1996). Customer segmentation for banks and insurance groups with fuzzy clustering techniques. Fuzzy Logic.
185. Wong, E. A. (1982). Statistical approach to incomplete information in data-base systems. ACM Transactions on Database Systems, 7, 479-488.
186. Wong, M., & Leung, K. (1990). A fuzzy database-query language. Information Systems, 15, 583-590.
187. Yager, R. R. (1980). On a general class of fuzzy connectives. Fuzzy Sets and Systems, 235-242.
188. Yager, R. R. (1983). Quantified propositions of a linguistic logic. International Journal of Man-Machine Studies, 19, 195-227.
189. Yager, R. R. et al. (1987). Fuzzy sets and applications: Selected papers by L. A. Zadeh. Wiley Interscience.
190. Yazici, A., & Akkaya, K. (2000). Conceptual modeling of geographic information system applications. In G. Bordogna & G. Pasi (Eds.), Recent issues on fuzzy databases (pp. 129-151). Physica-Verlag.
191. Yazici, A., Buckles, B. P., & Petry, F. E. (1999). Handling complex and uncertain information in the ExIFO and NF2 data models. IEEE Transactions on Fuzzy Systems, 7(6), 659-675.
192. Yazici, A., & Cinar, A. (2000). Conceptual modeling for the design of fuzzy object oriented database. In O. Pons, M. A. Vila, & J. Kacprzyk (Eds.), Knowledge management in fuzzy databases (pp. 12-35). Physica-Verlag.
193. Yazici, A., & George, R. (1999). Fuzzy database modeling. New York: Physica-Verlag.
194. Yazici, A., & Koyuncu, M. (1997). Fuzzy object-oriented database modeling coupled with fuzzy. Fuzzy Sets and Systems, 89, 1-26.
195. Yazici, A., & Merdan, O. (1996). Extending IFO data model for uncertain information. In Proceedings of the Fourth International Conference on Information Processing and Management of Uncertainty (IPMU'96, Vol. III), Spain (pp. 1282-1283).
196. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338-353.
197. Zadeh, L. A. (1971). Similarity relations and fuzzy orderings. Information Sciences, 3, 177-200.
198. Zadeh, L. A. (1975a). The concept of linguistic variable and its application to approximate reasoning: Part 1. Information Sciences, 8, 199-249.
199. Zadeh, L. A. (1975b). The concept of linguistic variable and its application to approximate reasoning: Part 2. Information Sciences, 8, 301-367.
200. Zadeh, L. A. (1975c). The concept of linguistic variable and its application to approximate reasoning: Part 3. Information Sciences, 9, 43-60.
201. Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1, 3-28.
202. Zadeh, L. A. (1983). A computational approach to fuzzy quantifiers in natural languages. Computer Mathematics with Applications, 9, 149-183.
203. Zadeh, L. A. (1992). Knowledge representation in fuzzy logic. In An introduction to fuzzy logic applications in intelligent systems. Kluwer Academic.
204. Zemankova-Leech, M., & Kandel, A. (1984). Fuzzy relational databases: A key to expert systems. Köln, Germany: Verlag TUV Rheinland.
205. Zemankova-Leech, M., & Kandel, A. (1985). Implementing imprecision in information systems. Information Sciences, 37, 107-141.
206. Zimmermann, H.-J. (1991). Fuzzy set theory and its applications (2nd ed.). Kluwer Academic.
207. Zvieli, A., & Chen, P. (1986). ER modeling and fuzzy databases. In Proceedings of the Second International Conference on Data Engineering (pp. 320-327).