Karen Lopez: To bring others up to speed, Fabian is using an academic taxonomy for data modeling terms. It's valid. It's popular in research. It's not used in any major data modeling tool, nor in many practitioner resources. I have been using the industry vernaculars in my posts and here. Part of the debate that Fabian is having is because he does not tolerate the industry terms, so he chooses to attack others who use the mainstream terms. He believes they are "wrong" instead of "alternative". So that's part of the pain we have in debating his positions. I'm bilingual, but I choose to use just one set in my writings.

Fabian Pascal: Karen, bollocks. See, I can use non-academic terms too.

NULL values can be very useful, especially on indexes, as an indication of "index is not set" or "no index here", or "default inherited index applies".

I use Null values extensively (in huge database systems) with not only no problems whatsoever, but measurably signifcant advantages. People who try to tell you that "Nulls are the work of the devil", or :the sky will fall down if you allow nulls", or some such unsubstantiated childish delusion are exclusively ignorant of the correct ways to handle them. (Or too lazy/ineducable to learn their correct implementation and/or benefits.)

Fact, just plain indisputable fact. --bytes.com

I don't know that I would say that the RM is dead per se, or even holding us back. But I have to ask... why the relational model? ...A Hierarchical Model would work. In fact if we look at these NoSQL databases ... Hierarchical Models work better than relational models. The point is that some of the factors which cause one to think in terms of relationships have changed. Disk is cheap. There are definitely problems where having a strongly typed and structured model make sense. Then there are problems where not having a rigid model make sense. Structure on read seems to make sense these days. PS... Relational models don't scale. Especially on MPP.

--LinkedIn.com

See:

SEMANTICS, DATABASE RELATIONS, AND TABLES

Table (n.) – a collection of information (data?) describing a population of entities which possess some common characteristics, called attributes. Tables are the building block of relational databases.  Tables must generally be “normalized,” at least to 1NF.  That may be an appropriate way to think of databases when implemented in a modern day DBMS.  However, it is not the way the world thinks logically. People have no problem with commonly occurring phenomena such as:
* A multi-valued attribute, e.g., an Employee possesses multiple Skills.
* Many-to-many (M:N) relationships, e.g., as between Employees and Projects
* A relationship with attributes
--Gordon Everest, Recognizing and Treating Tableitis