Data modeling

There are three different types of data models produced while progressing from requirements to the actual database to be used for the information system.

The data models should ideally be stored in a repository so that they can be retrieved, expanded, and edited over time.

This may occur when the quality of the data models implemented in systems and interfaces is poor.

In each case, of course, the structures must remain consistent across all schemas of the same data model.

[6] The process of designing a database involves producing the previously described three types of schemas – conceptual, logical, and physical.

The primary reason for this cost is that these systems do not share a common data model.

Therefore, an efficiently designed basic data model can minimize rework with minimal modifications for the purposes of different systems within the organization[1] Data models represent information areas of interest.

In addition, some CASE tools don't make a distinction between logical and physical data models.

The data modeling technique can be used to describe any ontology (i.e. an overview and classifications of used terms and their relationships) for a certain universe of discourse i.e. area of interest.

By standardization 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.

The logical data structure of a DBMS, whether hierarchical, network, or relational, cannot totally satisfy the requirements for a conceptual definition of data because it is limited in scope and biased toward the implementation strategy employed by the DBMS.

That is unless the semantic data model is implemented in the database on purpose, a choice which may slightly impact performance but generally vastly improves productivity.

As illustrated in the figure the real world, in terms of resources, ideas, events, etc., is symbolically defined by its description within physical data stores.

A semantic data model is an abstraction which defines how the stored symbols relate to the real world.

The data modeling process. The figure illustrates the way data models are developed and used today . A conceptual data model is developed based on the data requirements for the application that is being developed, perhaps in the context of an activity model . The data model will normally consist of entity types, attributes, relationships, integrity rules, and the definitions of those objects. This is then used as the start point for interface or database design. [ 1 ]
How data models deliver benefit. [ 1 ]
The ANSI/SPARC three level architecture. This shows that a data model can be an external model (or view), a conceptual model, or a physical model. This is not the only way to look at data models, but it is a useful way, particularly when comparing models. [ 1 ]
Data modeling in the context of business process integration. [ 6 ]
Example of an IDEF1X entity–relationship diagrams used to model IDEF1X itself. The name of the view is mm. The domain hierarchy and constraints are also given. The constraints are expressed as sentences in the formal theory of the meta model. [ 8 ]
Example of a Generic data model. [ 9 ]
Semantic data models. [ 8 ]