Monday, February 2, 2026

DATA MUDDLING



Chris Date once published an article at the old DBDebunk titled “Models, Models, Everywhere, Nor Any Time to Think”. If you want to get a hold of what he meant then, you oughta do a search on the title now and see what you get.

The continuous proliferation of models is an indication and measure of the disregard, if not outright hostility of the industry to sound theoretical foundations. It keeps reminding me of a decades-old piece I posted in response to David Hay's critique of Ron Ross's then proposal of a “fact model” (yet another one) as an alternative to data model. It is more relevant than ever, which is why I decided to bring it up to date. The problem is so entrenched and widespread, that even those who try to address it fail to realize that they are victims of it too.

Hay correctly observed:   

“In our industry, there is a strong desire to put names on things. This is natural enough, given the amount of information that we have to classify and deal with in our work. To give something a name is to gain control over it, and this is not necessarily a bad thing. The problem is when the name takes the place of true understanding of the thing named. Discourse tends to be the bantering of names, without true understanding of the concepts involved.” 

In this industry, many of the names are just re-labeling, whether it fits or not. Here are a couple of exquisite examples of both cases:

“I was amused to read in [Ralph Kimball's] article that my own suppliers and parts database design was "a perfect, beautiful star schema!" When I first learned the term "star schema", my reaction was that a properly designed star schema would be nothing neither more, nor less than a properly designed schema per se (in other words, one that did obey those scientific principles of relational design that do exist). So to see RK say that my schema was in fact a star schema reminded me (I’m afraid) of Peter Chen’s original E/R paper, in which—among other things—he reinvented the concept of domains, but called them value sets, and then went on to analyze the relational model in terms of his own ideas and said “Look, domains are just value sets!” --C. J. Date

Note: Kimball's "star schema" is, of course, not a relational schema, but quite an attempt to avoid it, due to failure to distinguish application views of the database from the database schema. 

Sunday, January 25, 2026

WHAT MEANING MEANS: BUSINESS RULES, PREDICATES, CONSTRAINTS, AND SEMANTIC CONSISTENCY



If we step back and look at what RDBMS is, we’ll no doubt be able to conclude that, as its name suggests (i.e., Relational Database Management System), it is a system that specializes in managing the data in a relational fashion. Nothing more. Folks, it’s important to keep in mind that it manages the data, not the MEANING of the data! And if you really need a parallel, RDBMS is much more akin to a word processor than to an operating system. A word processor (such as the much maligned MS Word, or a much nicer WordPress, for example) specializes in managing words. It does not specialize in managing the meaning of the words ... So who is then responsible for managing the meaning of the words? It’s the author, who else? Why should we tolerate RDBMS opinions on our data? We’re the masters, RDBMS is the servant, it should shut up and serve. End of discussion.” --Alex Bunardzik, Should Database Manage The Meaning?

Tuesday, January 21, 2025

Revision 1 (6/25) of CONCEPTUAL MODELING FOR DATABASE DESIGN - A Sound Guide



 


Table of Contents

Introduction
1 Information Representation
2 Conceptual Modeling
2.1 Ontological Commitment
2. 2 Properties and Relationships
3. Entity Properties  
3.1 First Order Properties
3.2 Assertion Predicates
3.3 Second Order Properties
4 Group Properties
4.1 Third Order Properties
4.1.1 Entity Uniqueness
4.1.2 1OP (in Context) Dependencies
4.1.3 Aggregates Relationships
4.1.4 Meaning Criteria and ESS Relationships
4.1.5 Designation “Property”
5 Multigroup Fourth Order Properties
5.1 Inter-group Entity Relationships
5.2 Inter-group Aggregates Relationships
6. Business Rules
6.1 Entity Type Rules
6.2 Group Type Rules
6.3 Multigroup Type Rules
Conclusion
Appendix: PoM/OCP and RDM





Revision 1 (7/25) of RELATIONAL DATABASE DOMAINS: A Definitive Guide






Table of Contents

Introduction
1 Formal Theory and Interpretation
2 Database Domains
2.1 Domains As Data Types
2.1.1 RDM and Programming Data Types
2.1.2 Abstract Data Types
2.2 Domain Definition
2.2.1 Type Specification
2.2.2 Domain Operators
3 Kinds of Domains
3.1 Base and Derived Domains
3.2 Primitive Domains
3.3 Atomic ("Simple") Domains
3.4 Complex (Derived) Domains
4 RDM Type System
5 DBMS Domain Support
Appendix 1: A Complex Domain Example
Appendix 2: A Note on SQL Built-in Data Types


 

 

 

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