Skip to main content
SpringerLink
Log in

An Overview of Fuzzy Data Models

  • Conference paper
Fuzziness in Database Management Systems

Part of the book series: Studies in Fuzziness ((STUDFUZZ,volume 5))

Abstract

Fuzzy databases provide means of representing, storing, and manipulating imprecise and uncertain information. This paper focuses on semantic and relational data models such as Chen’s entity-relationship model and Codd’s relational model and provides an overview of fuzzy extensions of these classical models that are generally incapable of dealing with imprecision, uncertainty, or partial knowledge. Primary attention is paid to the current state of fuzzy databases from the perspectives of conceptual modeling, data representation, database queries, and database design. This paper is considered as an update of the survey that appeared in JASIS (Kerre et al., 1986).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  • Anvari M.; Rose G.F., 1984, Fuzzy relational databases, Proceedings of the 1st Int. Conf. on FIP, Hawaii.

    Google Scholar 

  • Baldwin J.F.; Zhou S.Q., 1984, A fuzzy relational inference language, Fuzzy Sets & Systems 14,pp. 155–174.

    Article  Google Scholar 

  • Bosc P.; Galibourg M.: Hamon G., 1988, Fuzzy querying with SQL: extensions and implementation aspects, Fuzzy Sets and Systems, Vol. 28, 333–349.

    Google Scholar 

  • Bosc P.; Pivert 0., 199la, About equivalents in SQLf: a relational language supporting imprecise querying, Proceedings of International Fuzzy Engineering Symposium,Yokohama (Japan), 309–320.

    Google Scholar 

  • Bosc P.; Pivert O, 1991b, Fuzzy querying in conventional databases, in Zadeh. L.; Kacprzyk,J.,(eds.), Fuzzy Logic for the Management of Uncertainty. John Wiley & Sons, Inc., New York.

    Google Scholar 

  • Buckles B.P.; Petry F.E., 1982, A fuzzy representation of data for relational databases. Fuzzy Sets & Systems 7,pp. 213–226.

    Article  Google Scholar 

  • Buckles B.P.; Petry F.E., 1984, Generalized Databases and Information Systems. tech.report CSE,uni.of Texas at Arlinton.

    Google Scholar 

  • Buckles B.P.; Petry F.E.; Sachar H.S., 1986, Design of similarity-basedrelational databases. In H.Prade & C.V.Negoita(eds), Fuzzy Logic in Knowledge Engineering (pp.3–7). Verlag TUV Rheinland.

    Google Scholar 

  • Buckles B.P.; Petty F.E.; Sachar H.S., 1989, A domain calculus for fuzzy relational databases, Fuzzy Sets and Systems, 29,pp. 327–340.

    Article  Google Scholar 

  • Chen G.Q., 1989, Knowledge Representation with uncertainty and fuzzy data modeling (a survey), research paper No. 9005, ETEW, K.U.Leuven, Belgium.

    Google Scholar 

  • Chen G.Q., 1991a, A step towards the theory of fuzzy relational database design, Proceedings of IFSA’91 World Congress, pp44–47. July 7–12, Brussels.

    Google Scholar 

  • Chen G.Q., 1991b, Fuzzy data modeling: perspectives, problems and solutions, in E.E.Kerre (ed.), Introduction to the Basic Principles of Fuzzy Set Theory and Some of Its Applications, pp 294–343, Communication & Cognition, Gent, Belgium.

    Google Scholar 

  • Chen G.Q.; Kerre E.E.; Vandenbulcke J., 1992, Fuzzy functionaldependency and its axiomatic system, Proceedings of the Fourth International Conference on Information Processing and Management of Uncertainty (IPMU’92), pp 313–316, July, Palma (Spain).

    Google Scholar 

  • Chen G.Q.; Kerre E.E.; Vandenbulcke J., 1993a (to appear), A computational algorithm for the FFD closure and a complete axiomatization of Fuzzy functional dependency (1+D)International Journal of Intelligent Systems.

    Google Scholar 

  • Chen G.Q.; Kerre E.E.; Vandenbulcke J., 1993b, The dependency-preserving decomposition and a testing algorithm in a fuzzy relational data model, submitted to Fuzzy Sets and Systems.

    Google Scholar 

  • Chen G.Q.; Kerre E.E.; Vandenbulcke J., 1994 (to appear), Fuzzy normal forms and a dependency-preserving decomposition into 0-F3NFProc. of IEEE World Congress on Computational Intelligence (WCCI’94: FUZZ-IEEE’94).

    Google Scholar 

  • Chen G.Q.; Vandenbulcke J.; Kerre E.E., 1992, A general treatment of data redundancy in a fuzzy relational data model, Journal of The American Society for Information Science, 43, pp 304–311.

    Article  Google Scholar 

  • Chen G.Q.; Vandenbulcke J.; Kerre E.E., 1993, On the lossless-join decomposition in a fuzzy relational data model, Proceedings of the Second International Symposium on Uncertainty Modeling and Analysis (ISUMA’93), pp. 440–446, April, Maryland (USA).

    Google Scholar 

  • Chen P.P., 1976, The entity-relationship model: towards a unified view of data, ACM trans. on Database Systems (1)1,pp. 9–36.

    Google Scholar 

  • Codd E.F., 1970, A relational model for large shared data banks, Comm. ACM (13)6, pp. 377–387.

    Google Scholar 

  • Cubero J.C.; Vila M.A., 1992, A new definition of fuzzy functional dependency in fuzzy relational databases, Proceedings of the Fourth International Conference on Information Processing and Management of Uncertainty (IPMU’92), pp 239–242, July, Palma (Spain).

    Google Scholar 

  • Date C.J., 1983, An Introduction to Database Systems. Vol.2 AddisonWesley(Mass.).

    Google Scholar 

  • Date C.J., 1986, An Introduction to Database Systems. 4th ed. Vol.! Addison-Wesley.

    Google Scholar 

  • Dubois D.; Prade H., 1992, Generalized dependencies in fuzzy databases, Proceedings of the Fourth International Conference on Information Processing and Management of Uncertainty (IPMU’92), pp 263–266, July, Palma (Spain).

    Google Scholar 

  • Kacprzyk J.; Zadrozny S.; Ziolkowski A., 1989, FQUERY III+: a “human-consistent” database querying system based on fuzzy logic with linguistic quantifiers, Information Systems, Vol.14, No.6, 443–453. springer-verlag.

    Google Scholar 

  • Kacprzyk J.; Ziolkowski A., 1986, Database queries with fuzzy linguistic quantifiers, IEEE Trans. on Sys. Man, and Cybern., 16: 474–479.

    Article  Google Scholar 

  • Kerre E.E.; Zenner R.B.R.C.; De Caluwe R.M.M., 1986, The use of fuzzy set theory in information retrieval and databases:a survey. Journal of the American Society for Information Science, 37 (5), pp. 341–345.

    Google Scholar 

  • Kerre E.E., 1988, Fuzzy Sets and Approximate Reasoning. Lecture notes for the course(special topics in computer sciences),University of Nebraska, Lincoln, USA.

    Google Scholar 

  • Kerre E.E.(ed.), 1991, Introduction to the Basic Principles of Fuzzy Set Theory and Some of Its Applications,Communication & Cognition, Gent, Belgium, 360p (Second Revised Edition, 1993).

    Google Scholar 

  • Kerre E.E., 1992, A comparative study of the behavior of some popular fuzzy implication operators on the generalized modus ponens, in Zadeh, L.; Kacprzyk,J.,(ed.), Fuzzy Logic for the Management of Uncertainty (pp. 281–296 ). John Wiley & Sons, Inc., New York.

    Google Scholar 

  • Kiss A., 1990, X-decomposition of fuzzy relational databases, Proceedings of International Workshop on Fuzzy Sets and Systems, December, Visegrad (Hungray).

    Google Scholar 

  • Prade H.; Negoita C.V.(eds), 1986, Fuzzy Logic in Knowledge Engineering. Verlag TUV Rheinland.

    Google Scholar 

  • Prade H.; Testemale C., 1983, Generalizing database relational algebra for the treatment of imcomplete/uncertain information and vague queries. Proceedings of 2nd NAFIPS Workshop, Schenectady, NY.

    Google Scholar 

  • Put F., 1982, Introducing Dynamic and Temporal Aspects in a Conceptual (Database) Schema. Doctoral Dissertation, No. 68, ETEW, K.U.Leuven, Belgium.

    Google Scholar 

  • Raju K.V.S.V.N.; Majumdar A.K., 1987, The study of joins in fuzzy relational databases, Fuzzy Sets & Systems, 21, pp. 19–34.

    Article  Google Scholar 

  • Rundensteiner E.A.; Hawkes L.W.; Bandler W., 1989, On nearness measures in fuzzy relational data models. Int. J. of Approximate Reasoning, _3, pp. 267–298.

    Google Scholar 

  • Ruspini E., 198.6, Imprecision and uncertainty in the entity-relationship model, in H.Prade & C.V.Negoita(eds), Fuzzy Logic in Knowledge Engineering (pp.18–22), Verlag TUV Rheinland.

    Google Scholar 

  • Shenoi S.; Melton A., 1989, Proximity relations in the fuzzy relational databases. Fuzzy Sets & Systems, 31, pp. 285–296.

    Article  Google Scholar 

  • Shenoi S.; Melton A.; Fan L.T., 1990, An equivalence classes model of fuzzy relational databases, Fuzzy Sets and Systems 38, pp. 153–170.

    Article  Google Scholar 

  • Testemale C., 1986, A database system dealing with incomplete or uncertain information and vague queries. In H.Prade & C.V.Negoita(eds), Fuzzy Logic in Knowledge Engineering (pp.29–45). Verlag TUV Rheinland.

    Google Scholar 

  • Tripathy R.C.; Saxena P.C., 1990, Multivalued dependencies in fuzzy relational databases, Fuzzy Sets & Systems 38, pp. 267–279.

    Article  Google Scholar 

  • Umano M., 1983, Retrieval from fuzzy databases by fuzzy relational algebra, in: Sanchez and Gupta (eds.), Fuzzy Information Knowledge Representation and Decision Analysis (pp. 1–6 ). Pergamon Press, Oxford, England.

    Google Scholar 

  • Ullman J.D., 1988, Principles of Database and Knowledge-base Systems,Vol.1, Computer sciences press inc.

    Google Scholar 

  • Van Schooten A., 1988, Design and Implementation of a Model for the Presentation and Manipulation of Uncertainty and Imprecision in Databases and Expert systems. Ph.D thesis(in Dutch), University of Gent, Belgium.

    Google Scholar 

  • Vandenberghe R.M., 1991, An extended entity-relationship model for fuzzy databases based on fuzzy truth values, Proceedings of IFSA’91 World Congress, pp 280–283, July, Brussels.

    Google Scholar 

  • Vandenberghe R.; Van Schooten A.; De Caluwe R.; Kerre E.E., 1989, Some practical aspects of fuzzy database techniques: an example. Information Systems, 6 (14).

    Google Scholar 

  • Yager R.R., 1984, General multiple-objective decision functions and linguistically quantified statements, Int. J. Man-Machine Studies, Vol. 2, 389–400.

    Article  Google Scholar 

  • Yager R.R., 1988, On ordered weighted average aggregation operators in multicriteria decisionmaking, IEEE Trans. on Systems, Man and Cybernetics, Vol. 18, No. 1, 183–190.

    Article  Google Scholar 

  • Yager R.R., 1991, Fuzzy quotient operators for fuzzy relational databases, Proc. Int. Fuzzy Engineering Symp., Yokohama, Japan, 289–296.

    Google Scholar 

  • Zadeh L. A., 1983, The role of fuzzy logic in the management of uncertainty in expert systems, Fuzzy Sets and Systems, 11:199–227.

    Google Scholar 

  • Zemankova M., 1986, Implementing aspects of human reasoning in expert systems design, In H.Prade & C.V.Negoita(eds), Fuzzy Logic in Knowledge Engineering (pp.73–92). Verlag TUV Rheinland.

    Google Scholar 

  • Zemankova M.; Kandel A., 1984, Fuzzy Relational Database - a Key to Expert System. Verlag TUV Rheinland, Cologne.

    Google Scholar 

  • Zemankova M.; Kandel,A., 1985, Implementing imprecision information systems, Information Sciences, 37, pp. 107–141.

    Article  Google Scholar 

  • Zvieli A.; Chen P.P., 1985, Entity-relationship modeling and fuzzy databases, Proceedings of 2nd Conference on Data Engineering, LA.

    Google Scholar 

  • Zvieli A., 1986, A fuzzy relational calculus, In: L.Kerschberg,eds., Expert Database Systems, Proceedings of 1st International Conference, April 1–4, South Carolina, USA

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Applied Mathematics and Computer Sciences, University of Gent, Krijgslaan 281/S9, 9000, Gent, Belgium

    Etienne E. Kerre

  2. Section of Informatics, Department of Applied Economic Sciences, Catholic University of Leuven, Dekenstraat 2, 3000, Leuven, Belgium

    Guoqing Chen

Authors
  1. Etienne E. Kerre
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guoqing Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Ecole Nationale Supérieure de Sciences Appliquées et de Technologie (ENSSAT), IRISA, 6. rue de Kéramport, 22305, Lannion Cédex, France

    Patrick Bosc

  2. Polish Academy of Sciences, Systems Research Institute, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

Rights and permissions

Reprints and permissions

Copyright information

© 1995 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kerre, E.E., Chen, G. (1995). An Overview of Fuzzy Data Models. In: Bosc, P., Kacprzyk, J. (eds) Fuzziness in Database Management Systems. Studies in Fuzziness, vol 5. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1897-0_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-7908-1897-0_2

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-662-11805-4

  • Online ISBN: 978-3-7908-1897-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation