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Featured Articles

Is There Gray When It Comes To Technology Independence for Ontology Models, Semantic Models, and Logical Data Models?

When it comes to ontologies, semantic models (conceptual data models), and logical data models, as well as the conceptual and logical architectures they represent, achieving technology independence can be a little tricky. Often, there is a lot of gray when determining if these models or architectures are truly technology independent. It can also be a matter of great discussion - and disagreement. Sometimes the line between being technology independent and technology dependent is clear, and sometimes it is a little blurry, especially when it comes to the primarily technology constrained modeling notations and tools available on the market. Sometimes, however, the meaning of technology independence is simply misunderstood.

What Does "Independent of Technical and Implementation Considerations" Really Mean?    

The statement that "conceptual and logical architectures or models are independent of technical and implementation considerations" is often misunderstood. It does not mean modeling at these levels is not "technical" or that advanced technology is

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Category: Information Architecture

Data Normalization Is Not Just About Relational Models or Databases

Data normalization is about normalizing logic. Unfortunately, data normalization is often incorrectly considered to be a process only applied to physical models or physical databases, specifically, relational models and databases. There are just as many benefits, if not more, to normalizing semantic models (conceptual data models) and logical data models as there are to normalizing physical data models or databases. The highest number of benefits are realized when ontology or other semantic models and logical data models are normalized to at least 4th normal form (4NF). Some data architects even prefer 5th normal form (5NF).

Ironically, in this day and age almost all physical models and databases are DE-normalized, typically for performance reasons. At least this has been true at most of the companies where I have worked. This is especially true for specialized databases such as data warehouse and data marts. Therefore, to say that normalization only applies to relational physical models or databases is being very shortsighted and ignorant (meaning lacking awareness or knowledge).

Who Invented Data Normalization?

I have read articles that incorrectly claim normalization was invented by the father of relational modeling. Codd may have been the first one to introduce the concept of using mathematical systems to construct more sound relational models, and he

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Category: Information Architecture

Article Preface

Many of us who are experienced in creating enterprise architectures and the models that represent them have at least 2 fundamental beliefs.  (1) Whether implicitly or explicitly, in order for a business to operate the entire enterprise architecture exists.  (2) There are important differences between architectures and models.   However, in this article those differences are irrelevant as the majority of the concepts and the primary messages discussed in this article apply to both architectures and models.  Therefore, for brevity’s sake, and to avoid arguments about whether the word “architecture” is being used correctly, the word “model” or models will be used in this article in place of the phrases “architecture and/or model”, “model and/or architecture”, and their plural versions. 

The phrase “semantic model” will be used to refer to a model of the

ontology of an enterprise.  This model is sometimes referred to as a “concept model”, a "fact model”, a “terms and facts model”, or even a "conceptual data model".  However, in this article, the term” semantic model” is used to represent the primary concept expressed by all of these terms.

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Category: Information Architecture