Abstract
In this chapter we show that entity and relationship types may be base, derived, or hybrid (Sect. 8.1). The instances of base types need to be explicitly represented in an information base, while those of derived and hybrid types may be inferred by an information system, using derivation rules. Derivation rules are domain knowledge that an information system needs in order to derive certain facts; this knowledge must therefore be described in the conceptual schema. Section 8.2 describes the logical and the UML representations of derived and hybrid types and their derivation rules. In general, derivation rules are very diverse, although certain kinds appear very often. Section 8.3 describes some of these. Section 8.4 shows that the derivation rules of constant relationships require special interpretation. Section 8.5 explains how to define a particular kind of hybrid type in UML. Derived types add complexity to a schema, so their definition must be justified. Section 8.6 deals with the justification of derived types.
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8.7 Bibliographical Notes
Bancilhon F, Ramakrishnan R (1986) An amateur’s introduction to recursive query processing strategies. SIGMOD Conference 1986:16–52.
Bergamaschi S, Sartori C (1992) On taxonomic reasoning in conceptual design. ACM Trans. Database Syst. 17(3):385–422.
Borgida A (1995) Description logics in data management. IEEE Trans. Software Eng. 7(5):671–682.
Brachman RJ, Schmolze JG (1985) An overview of the KL-ONE knowledge representation system. Cognitive Sci. 9(2):171–216.
Business Rules Group (2000) Defining business rules — What are they really? Final Report, July 2000, http://www.businessrulesgroup.org/first_paper/br01c0.htm
Ceri S, Fraternali P (1997) Designing database applications with objects and rules: The IDEA methodology. Addison-Wesley.
Gustaffsson MR, Karlsson T, Bubenko JA Jr. (1982) A declarative approach to conceptual information modeling. In: Olle et al. (eds) pp 93–142.
Halpin T (2001) Information modeling and relational databases: From conceptual analysis to logical design. Morgan Kaufmann.
Hammer M, McLeod D (1981) Database description with SDM: A semantic database model. ACM Trans. Database Syst. 6(3):351–386.
Hull R, King R (1987) Semantic database modeling: Survey, applications, and research issues. ACM Comput. Surv. 19(3):201–260.
Martin J, Odell J (1995) Object-oriented methods: A foundation. Prentice Hall. Chap. 6.
Nicolas JM, Gallaire H (1978) Data base: Theory vs. interpretation. In: Gallaire H, Minker J (eds) Logic and data bases. Plenum Press, pp 33–54.
Nijssen GM, Halpin TA (1989) Conceptual schema and relational database design. Prentice Hall. p. 130.
Olivé A (2003a) Derivation rules in object-oriented conceptual modeling languages. CAiSE 2003, LNCS 2681:404–420.
OMG (2005b) OCL 2.0 Specification. Version 2.0, ptc/2005-06-06.
Parsons J (1996) An information model based on classification theory. Management Science, 42(2):1437–1453.
Pastor O, Gómez J, Insfrán E, Pelechano V (2001) The OO-Method approach for information systems modeling: From object-oriented conceptual modeling to automated programming. Inf. Syst. 26:507–534.
Schreiber G, Akkermans H, Anjewierden A, Hoog R de, Shadbolt N, Van de Velde W, Wielinga B (2000) Knowledge engineering and management: The CommonKADS methodology. MIT Press. p. 98
Shipman DW (1981) The functional data model and the data language DAPLEX. ACM Trans. Database Syst. 6(1):140–173.
Warmer J, Kleppe A (2003) The Object Constraint Language: Getting your models ready for MDA, 2nd edn. Addison-Wesley.
Yourdon (1993) Yourdon systems method: Model-driven systems development. Yourdon Press. p. 61.
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(2007). Derived Types. In: Conceptual Modeling of Information Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39390-0_8
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DOI: https://doi.org/10.1007/978-3-540-39390-0_8
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