Generic data model
Generic data models are generalizations of conventional data models. They define standardised general relation types, together with the kinds of things that may be related by such a relation type. Overview. The definition of generic data model is similar to the definition of a natural language. For example, a generic data model may define relation types such as a 'classification relation', being a binary relation between an individual thing and a kind of thing (a class) and a 'part-whole relation', being a binary relation between two things, one with the role of part, the other with  the role of whole, regardless the kind of things that are related. Given an extensible list of classes, this allows the classification of any individual thing and to specify part-whole relations for any individual object. By standardisation of an extensible list of relation types, a generic data model enables the expression of an unlimited number of kinds of facts and will approach the capabilities of natural languages. Conventional data models, on the other hand, have a fixed and limited domain scope, because the instantiation (usage) of such a model only allows expressions of kinds of facts that are predefined  in the model. History. Generic data models are developed as an approach to solve some shortcomings of conventional data models. For example, different modelers usually produce different conventional data models of the same domain. This can lead to difficulty in bringing the models of different people together and is an obstacle for data exchange and data integration. Invariably, however, this difference is attributable to different levels of abstraction in the models and differences in the kinds of facts that can be instantiated (the semantic expression capabilities of the models). The modelers need to communicate and agree on certain elements which  are to be rendered more concretely, in order to make the differences less significant. Generic data model topics. Generic patterns. There are generic patterns that can be used to advantage for modeling business. These include entity types for PARTY (with included PERSON and ORGANIZATION), PRODUCT TYPE, PRODUCT INSTANCE, ACTIVITY TYPE, ACTIVITY INSTANCE, CONTRACT, GEOGRAPHIC AREA, and SITE. A model which explicitly includes versions of these entity classes will be both reasonably robust and reasonably easy to understand. More abstract models are suitable for general purpose tools, and consist of variations on THING and THING TYPE, with all actual data being  instances of these. Such abstract models are on one hand more difficult to manage, since they are not very expressive of real world things, but on the other hand they have a much wider applicability, especially if they are accompanied by a standardised dictionary. More concrete and specific data models will risk having to change as the scope or environment changes. Approach to generic data modeling. One approach to generic data modeling has the following characteristics: This way of modeling allows the addition of standard classes and standard relation types as data (instances), which makes the data model flexible and  prevents data model changes when the scope of the application changes. Generic data model rules. A generic data model obeys the following rules]: Examples. Examples of generic data models are References. 1. David C. Hay. 1995. "Data Model Patterns: Conventions of Thought". (New York: Dorset House). 2. David C. Hay. 2011. "Enterprise Model Patterns: Describing the World". (Bradley Beach,New Jersey: Technics Publications). 3. Matthew West 2011. "Developing High Quality Data Models" (Morgan Kaufmann)