data modeling whitepaper

Corepula Method Data Modeling Naming Conventions

The Corepula Method recommends using widely adopted naming convention rules when naming data modeling objects. It is essential that an entity name consist of three components: the prefix, qualifier, and suffix.

  1. A prefix classifies (from a business perspective) the overall type of data represented by the entity.
  2. A qualifier describes the specifics of what is represented by the entity.
  3. A suffix is used to provide additional insights into the nature of the data contained in the entity.

The Corepula Method uses the following Method-specific category suffixes:

  1. CORE (_CORE) indicates that the entity represents a standard core entity type;
  2. HISTORIZED ATTRIBUTE (_HIST_ATTR) means that the entity represents a historized attribute entity type;
  3. NON-HISTORIZED ATTRIBUTE (_NON_HIST_ATTR) specifies that the entity represents a non-historized attribute entity type; and
  4. COPULA (_CPLA) shows that the entity represents a copula entity type.

An attribute is a logical depiction of a column in a database table. Attributes (and columns in the physical layer) follow similar naming conventions as entities/tables. They have three components: prime, qualifier, and class.

  1. A prime component specifies a major entity or subject area;
  2. A qualifier component adds additional refinement, qualification, and readability; and
  3. A class component clarifies the nature of the data.

For example, the sample attribute "Employee Annual Pay Amount" can be broken into the following components:

  1. Prime component: Employee;
  2. Qualifier components: Annual and Pay; and
  3. Class component: Amount.

When working with conceptual and logical data models, avoid creating unnecessary abbreviations; this may lead to confusion and problematic ambiguities. Most data models must be validated by the business. Poorly specified attribute and entity names create the potential for misunderstanding and inaccurate interpretations of the underlying data. If you express the meaning of each entity and column as clearly and succinctly as possible and follow the naming guidelines outlined above, your data models will become much clearer and more expressive.

For a more thorough introduction into Corepula Method, please download Corepula Method data modeling whitepaper (PDF)