Abstract
From a formal point of view, a constraint can be seen as a relationship that has to be satisfied. With respect to data and information modelling, constraints provide a declarative and elegant means to represent data in an efficient way. Moreover, constraints support the definition of data semantics. For example, the set of valid temperatures t on earth can be defined as ranging from a high of 57 degrees Celcius to -70 degrees Celcius, which can be described by the constraint t ∈ [-70°C, 57°C] or, using linear arithmetic, by (t ≥ -70°C) ∧ (t ≤ 57°C).
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References
Bosc P., Prade H. (1993) An Introduction to Fuzzy Set and Possibility Theory Based Approaches to the Treatment of Uncertainty and Imprecision in Database Management Systems. In: Proc. of the 2nd Workshop on Uncertainty Management in Information Systems: From Needs to Solutions, Catalina, California, USA.
Cattell R.G.G., Barry D. (2000) The Object Data Standard: ODMG 3.0. Morgan Kaufmann Publishers, San Francisco, USA.
de Caluwe R., de Tré G., Van der Cruyssen B., Devos F., Maesfranckx P. (2000) Time management in Fuzzy and Uncertain Object-Oriented Databases. In: Pons O., Vila M.A., Kacprzyk J. (Eds.) Knowledge Management in Fuzzy Databases, Studies in Fuzziness and Soft Computing, Physica-Verlag, Heidelberg, Germany, pp. 67–88.
de Cooman G. (1995) Towards a possibilistic logic. In: Ruan D. (Ed.) Fuzzy Set Theory and Advanced Mathematical Applications, Kluwer Academic Publishers, Boston, USA, pp. 89–133.
de Cooman G. (1999) From possibilistic information to Kleene’s strong multivalued logics. In: Dubois D., Klement E.P., Prade H. (Eds.) Fuzzy Sets, Logics and Reasoning about Knowledge, Kluwer Academic Publishers, Boston, USA, pp. 315–323.
de Tré G., de Caluwe R., Van der Cruyssen B. (2000) A Generalised ObjectOriented Database Model. In: Bordogna G., Pasi G. (Eds.) Recent Issues on Fuzzy Databases, Studies in Fuzziness and Soft Computing, Physica-Verlag, Heidelberg, Germany, pp. 155–182.
de Tré G. (2002) Extended Possibilistic Truth Values. International Journal of Intelligent Systems 17:427–446.
de Tré G. (2003) Enhancing the ODMG model: A framework based on constraints. In: Mastorakis N.E., Antoniou G.E., Manikopoulos C., Bojkovic Z., Gonos I.F. (Eds.) Recent Advances in Communications and Computer Science, Electrical and Computer Engineering Series, WSEAS press, pp. 398–406.
de Tré G., de Caluwe R. (2003) Modelling Uncertainty in Multimedia Database Systems: An Extended Possibilistic Approach. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 11(1):5–22.
de Tré G., de Caluwe R. (2003) Level-2 fuzzy sets and their usefulness in objectoriented database modelling. Fuzzy Sets and Systems 140(1):29–49.
Dubois D., Prade H. (1988) Possibility Theory. Plenum Press, New York, USA.
Gottwald S. (1979) Set theory for fuzzy sets of higher level. Fuzzy Sets and Systems 2(2):125–151.
Kanellakis P.C., Kuper G.M., Revesz P.Z. (1995) Constraint Query Languages. Journal of Computer and System Sciences 51:26–52.
Kuper A., Libkin L., Paredaens J. (Eds.) (1993) Constraint Databases. Springer-Verlag, Berlin, Germany.
Lausen G., Vossen G. (1998) Models and Languages of Object-Oriented Databases. Addison-Wesley, Harlow, England.
Morris A. (2001) Why Spatial Databases Need Fuzziness. In: Proc. of Joint 9th IFSA and 20th NAFIPS International Conference, Vancouver, Canada, pp. 2446–2451.
Motro A. (1995) Management of Uncertainty in Database Systems. In: Kim W. (Ed.) Modern Database Systems, The object model, interoperability and beyond, Addison-Wesley Publishing Company, Reading, Massachusetts, USA.
Paredaens J., De Bra P., Gyssens M., Van Gucht D. (1989) The Structure of the Relational Database Model. Springer-Verlag, Berlin, Germany.
Parsons S. (1996) Current Approaches to Handling Imperfect Information in Data and Knowledge Bases. IEEE Transactions on Knowledge and Data Engineering 8(3):353–372.
Prade H. (1982) Possibility sets, fuzzy sets and their relation to Lukasiewicz logic. In: Proc. of the 12th International Symposium on Multiple-Valued Logic, pp. 223–227.
Prade H. (2001) From flexible queries to case-based evaluation. In: Proc. of ECSQARU-2001 workshop on Management of uncertainty and imprecision in multimedia information systems, Toulouse, France.
Rescher N. (1969) Many-Valued Logic. McGraw-Hill, New York, USA.
Riedel H., Scholl M.H. (1997) A Formalization of ODMG Queries. In: Spaccapietra S., Maryanski F. (Eds.) Proc. of the 7th Working Conference on Database Semantics (DS-7), Leysin, Swiss, pp. 63–90.
Rigaux P., Scholl M., Voisard A. (2002) Spatial Databases with Application to GIS. Morgan Kaufmann Publishers, San Francisco, USA.
Shekhar S., Chawla S. (2003) Spatial Databases: A Tour. Pearson Education Inc., New Jersey, USA.
Taivalsari A. (1996) On the Notion of Inheritance. ACM Computing Surveys 28(3):438–479.
Tansel A., Clifford J., Gadia S., Jajodia S., Segev A., Snodgrass R. (1993) Temporal Databases: Theory, Design, and Implementation. The Benjamin/Cummings Publishing Company Inc., Redwood City, USA.
Zadeh L.A. (1965) Fuzzy sets. Information and Control 8(3):338–353.
Zadeh L.A. (1968) Probability measures of fuzzy events. Journal of Mathematical Analysis and Applications 23:421–427.
Zadeh L.A. (1975) The concept of linguistic variable and its application to approximate reasoning Parts I, II and III. Information Sciences 8:199–251, 301–357 9:43–80.
Zadeh L.A. (1978) Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems 1(1):3–28.
Zadeh L.A. (1986) Outline of a computational approach to meaning and knowledge representation based on a concept of a generalized assignment statement. In Thoma M., Wyner A. (Eds.) Proc. of the International Seminar on Artificial Intelligence and Man-Machine Systems, Springer, Heidelberg, Germany, pp. 198–211.
Zadeh L.A. (1997) Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems 90(2):111–127.
Zadeh L.A. (1999) From computing with numbers to computing with words— from manipulation of measurements to manipulation of perceptions. IEEE Transactions on Circuit Systems 45:105–119.
Zadeh L.A. (2002) Toward a preception-based theory of probabilistic reasoning with imprecise probabilities. Journal of Statistical Planning and Inference 105:233–264.
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de Tré, G., de Caluwe, R., Verstraete, J., Hallez, A. (2004). The Applicability of Generalized Constraints in Spatio-Temporal Database Modelling and Querying. In: de Caluwe, R., de Tré, G., Bordogna, G. (eds) Spatio-Temporal Databases. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-09968-1_7
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DOI: https://doi.org/10.1007/978-3-662-09968-1_7
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