The Universe of Discourse


This is another framework I often refer to. I call it the “Moffett Framework” based on James Moffett’s explanation of the “Universe of Discourse”.


I found it can be represented graphically:


Where the vertical axis is speaker and audience and the horizontal axis is time and senses. I will discuss this further in a latter post.

Systema: Zachman Framework Abstract


The Zachman Framework I have seen diagrammed often. However, I have not found the diagrams any more enlightening than the original diagrams by John Zachman. The reason is because the diagrams referred to other diagramming conventions instead of revealing the underlying concepts being described by the diagrams. Click on the thumbnail below to view what I consider an appropriate abstraction of the Zachman Framework:


I have submitted this diagram to John Zachman and he has welcomed it.

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Twelve Dimensional Universe

In information technology there are four common components to every system: process, data, location and events. When we define the structure of these components, each is a three dimensional network. As a business system operates, each of these networks is negotiated and each creates its own three dimensional path. Any observation of the system is an intersection of the paths of the four components. It lead me to wonder why the same paradigm couldn’t be transplanted where process is energy, data is mass and location and event are the one dimensional coordinates of space and time. If so, the universe could be viewed as four three dimensional networks that we traverse in four three dimensional paths with any observation being the intersection of those four paths. The universe was twelve dimensional with the only constant being change.

This is my thought experiment for time:

1. The acceleration of everything in the universe is constantly changing

2. Therefore every photon that strikes me is of a different wavelength

3. I am not one electron nor is my source of photons one electron

4. Therefore it is impossible for every photon I observe to come from the same source electron or strike the same target electron

5. Therefore it is impossible for me to observe from a single coordinate system or observe to a single coordinate system

6. Time is not linear

7. Therefore the speed of light is not constant because it cannot be measured with precision or accuracy.

The observer never observes the same clock.

The observer never observes the same energy.

The observer never observes the same mass.

The observer never observes the same location.

Abandon statistics for a moment. Everything in the universe is unique. In fact, every atomic and subatomic particle is as unique as a snowflake.

My theory is that energy, mass, space and time are each three dimensional networks that collapse into each unique observation. The universe is twelve dimensional.

(Ex,Ey,Ez) = (mx,my,mz)(Δ(lx,ly,lz)/Δ(ex,ey,ez))2

Where E is energy, m is mass, l is location and e is event.

Give Michio Kaku a view.

Data, Information, Knowledge and Wisdom

In the late 1960s a Harvard English professor, James Moffett, wrote a book by the title Teaching the Universe of Discourse. In it he classified four perspectives that one can take to any system. These perspectives were:

Observing: What is happening

Reporting: What happened

Generalizing: What happens

Theorizing: What may happen

The products of the perspectives have been popularly termed:

  1. Data
  2. Information
  3. Knowledge
  4. Wisdom

Understanding these four perspectives and their deliverables give us an understanding of the capabilities that each affords.

Data is the set of variables that represent any state of a system such as a business. Each time something changes in a system its state changes. It is crucial to know what the state changes are and how to measure them. In science, these are known as variables.  In databases these are known as instances.  It can also be termed “polation”.

Information is the collection of data as a system’s states change. This permits the comparison of each state. In science, these are known as the results.  In databases these are represented as tables.  It can also be termed “intrapolation.”

Knowledge is the ability to generalize what a system’s states will be given particular data and information. In science, this is known as hypothesis.  In database parlance these are known as relationships.  It can also be called “interpolation”.

Wisdom is the ability to turn exceptions into advantage—states that are missed by current data, information and knowledge. It is necessary to compare many states of one form of information to many states of another form.  The result is used for projection into the future. In science, this is known as new theory. Database modelers treat this as a many to many relationship and resolve it as an association.  It is also known as “extrapolation”.

I want to distinguish the model for a venture from the model of a business. One of the key obstacles to innovation is communicating a venture model to minds grown comfortable with a business model. First, the people we are trying to convince may not even know there is an exception. Second, the new data, information, knowledge and wisdom we advocate are all untested. We do not know if the data we advocate measures the new states. We do not know if the information will make comparison of all states possible. We do not know if our knowledge will successfully forecast the benefits of the new states. We do not know if our wisdom will be able to handle new exceptions spawned by the venture. No matter how much you analyze, ultimately the experiment has to be performed. As in science, the scale of the experiment dictates the risk.

The Classification of Exceptions

Exceptions in a system are not a one dimensional phenomena, however for the first part of this discussion I am only going to concentrate on a single dimension. This dimension has the following six categories:

  1. Conceptual
  2. Contextual
  3. Logical
  4. Physical
  5. Mechanical
  6. Operational

These categories descend from largest, conceptual, to smallest, operational.

Conceptual exceptions involve the failure of the business model to recognize an achievable objective. Simply put, something new can be done. A contextual exception is the failure of the business model to recognize a consumer context it can support. It can be done and it can be done for the customer base chosen. A logical exception is a failure of the business model to recognize a product or service the customer requires consistently. A physical exception is a failure of the business model to recognize an opportunity to deliver to the customer at a lower cost. A mechanical exception is not seeing how to correct a defect in the manufacture of a batch of product or in the training in the procedures of a service. An operational exception is correcting a defect in a single use of a product or service.

Dealing with exceptions in only the context of product and service is actually in many instances insufficient. There are several more facets to any business and any system. For example

  • Product

  • Service

  • Employee

  • Location

  • Timing

  • Need

When we look at these facets and consider them in combination with the dimension we have already discussed, suddenly a venture becomes increasingly complex. The conceptual category now begs the questions: Is the product possible? Is the service possible? Can we find the employees to make it possible? Is there a location where it is possible? Is there an appropriate time for this venture? Is there a need for the product or service of this venture? And there are still five more categories unexplored.

What we have with these two dimensions are thirty-six different types of exceptions a business model can have.

The Dip Slips

In his new book, The Dip, “marketing guru” Seth Godin attempts to use the Pareto principle to claim that the first 20 percent of the work derives 80 percent of the benefit. I think he has missed the mark entirely.

If we look at the exceptions I have detailed above in the context of the Pareto principle we can see that the first four categories, which comprise eighty percent of the work, provide only twenty percent of the benefit. The first four categories are design categories. The last two categories are development and use. It is the Mechanical and Operational categories that deliver eighty percent of the profit. Put another way, “Measure twice, cut once.” Seth Godin, has logically put the cart before the horse.

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Join In

I came across this simple explanation of joins.

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Surrogate Key Best Practice

Surrogate keys (unintelligent keys) are an excellent way to preserve the referential integrity of your database and avoid the need for cascading updates of natural keys if they are changed. However, simply using surrogate key is not always enough.

One of the most common best practices for databases designed with surrogate keys is: Any independent table with a surrogate primary key should have a corresponding natural key with uniqueness enforced. When uniqueness is not enforced duplicate rows are possible and your referential integrity is lost.

For example, consider the table below:


In the student table the primary key is a surrogate key, but uniqueness is enforced stringently by protecting the full name of the student and his birthdate as a unique composite natural key.

Another context for protecting surrogate key referential integrity is by protecting the uniqueness of foreign key combinations where uniqueness would be enforced if the table were dependent. For example consider the tables below:


Note how the enrollment table has a surrogate primary key, however uniqueness is protected by the unique natural key index on the two foreign keys.

If you use surrogate keys and natural keys together when you design your database, you will avoid the pitfalls of duplicate rows in your data.

Data Operation Language Icons

I have been through an endless number of applications and I have never seen a set of icons that I really liked to represent data query, data definition and data manipulation in SQL. Finally, I decided to create a convention of my own.



I attempted to come up with a clearly distinct image for each operation. I also would like to apply these foundation icons consistently to create a complete set of DQL, DDL and DML icons. For example, below is the set of icons for operations on databases:




The same applied to tables:




And to columns of tables:




As you can see, the metaphor is consistent and communicative. I will be working on more icons for future posts.





Platonic Dimensional Models

I have been spending some time reading Synergetics by R. Buckminster Fuller and it has me thinking about the structures of dimensional databases.

My work as a data modeler has exposed me to many ontologies. Every data model you create is a self contained ontological framework. And for a long time I did not think about what those frameworks had in common. Every project was unique.
In the course of my recreational reading I learned about Pattern Language from Christopher Alexander, saw its adaptation in object oriented design and over time learned how patterns could exist in relational models to address particular applications most effectively. It became obvious that there were optimal patterns for competitive advantage and the seemingly limitless array of patterns for databases revealed optimals and families of optimals.

In studying dimensional data model design, based on Ralph Kimball’s work, I found myself also looking for patterns. The only case of patterning I found at first were called the Basic Interrogatives. Further research corroborated this pattern by way of the Zachman Framework. There was a six dimensional pattern. But at that point I stalled.

Several years later I began to read the works of R. Buckminster Fuller and a new world of possibility opened up to me. What Fuller led me to do was visualize dimensional models not in the traditional two dimensional star or snowflake, but as polyhedra with the vertices representing each of the dimensions and the fact being the center of gravity of the solid.

The fit of the basic interrogatives into an octahedral structure lead me to wonder about polyhedra further. Fuller’s work lead me to think about the platonic solids and other families of polyhedra. I began to see patterns in dimensional data models similar to the six interrogatives.

My search for generic dimensional models lead me to the world of library science. I began to look at classification and the work of S. Raganathan came to light. Colon Classification presented another generic structure and this pointed to Bliss Classification 2. Colon Classification presented four dimensions, the Basic Interrogatives presented six dimensions and BC2 presented twelve dimensions. These generic classification systems correlated with the tetrahedron, octahedron and icosahedron–the triangulated Platonic solids.


This is as far as I have come. I am now looking for more dimensional models to examine for patterns that correspond to polyhedra.

Hello world!

Welcome to my new blog.

My name is Grant Czerepak. I am an IT professional with over 20 years experience in relational database technology specifically in the areas of design, development and administration. I have worked across Canada, the United States as well as in Singapore.

In this blog I will be mixing, matching, shifting and sifting paradigms that have come up in my work with relational database and other concepts. I hope to make entries daily and keep this blog interesting and stimulating.

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