Universe: The Fabrics of Perception


I am working with the Latin language and it is helping me to classify my thoughts more effectively by understanding historical correlations in meaning. For example matter was considered a fabric. The term for light, “lume”, comes from the term loom which alludes to textile manufacture. In fact all of the textile terms merge with geometry where they were practically applied.

WEAVE: a fabric
POINT: a intersection
LINE: a line
ANG: a cut
HEIR: an area
VOL: layers
QUAL: a bundle

These terms have influenced our thinking for literally thousands of years. We still talk of the “fabric” of space, the fabric of time and “material” or whatever. We are unintentionally applying a metaphor. Yet it is a metaphor that has served us well.

At this point I present a scale that I have arrived at for human sensory perception.


– 8 , – 2 , – 1 , 0 , + 1 , + 2 , + 8


8 is infinity

2 is two

1 is one

0 is zero

+ is positive

– is negative

– 8 : WEAVE below perception: Datrice
– 2 : POINT: below acception: Sortrice
– 1 : LINE: below exception: Matrice
0 : ANGE: exception: Natrice
+ 1 : HEIR above exception: Patrice
+ 2 : VOL: above acception: Fratrice
+ 8 : QUAL: above perception: Satrice

1. WHO: Eyes: Occipital Lobe: Speciatation of Matter.

+ 8 , + 2 , + 1 , 0 , – 1 , – 2 , – 8

Standard prefixes with root ASTR for the night sky:

– 8 : WEAVISTER: below perception
– 2 : POINTISTER: below acception
– 1 : LINISTER: below exception
0 : ANGISTER: exception
+ 1 : HEIRISTER above exception
+ 2 : VOLISTER: above acception
+ 8 : QUALESTER: above perception

PhotonicPhotons, PhotonicElectrons, PhotoincIons, PhotonicGases, PhotonicLiquids, PhotonicSolids, PhotonicMolecules

2. WHAT: Ears: Temporal Lobe: Association of Matter

+ 8 , + 2 , + 1 , 0 , – 1 , – 2 , – 8

Standard prefixes with root FUL for Electricity or “Lightning” which is interesting because it means we hear events.

– 8 : WEAVIFUL: below perception
– 2 : POINTIFUL: below acception
– 1 : LINIFUL: below exception
0 : ANGIFUL: exception
+ 1 : HEIRIFUL: above exception
+ 2 : VOLIFUL: above acception
+ 8 : QUALIFUL: above perception

ElectronicPhotons, ElectronicElectons, ElectronicIons, ElectronicGases, ElectronicLiquids, ElectronicSolids, ElectronicMolecules

3. WHEN: Nose: Brainstem: Attibution of Matter

+ 8 , + 2 , + 1 , 0 , – 1 , – 2 , – 8

Standard prefixes with root FIED for Ions or burn which is interesting because it means we smell ions or things that are reactive.

– 8 : WEAVEFIED: below perception
– 2 : POINTFIED: below acception
– 1 : LINEFIED: below exception
0 : ANGFIED: exception
+ 1 : HEIRFIED: above exception
+ 2 : VOLFIED: above acception
+ 8 : QUALIFIED: above perception

IonicPhotons, IonicElectrons, IonicIons, IonicGases, IonicLiquids, IonicSolids, IonicMolecules

4. WHERE: Throat: Parietal Lobe: Domination of Matter

+ 8 , + 2 , + 1 , 0 , – 1 , – 2 , – 8

Standard prefixes with root AER for Gases

– 8 : WEAVIER: below perception
– 2 : POINTIER: below acception
– 1 : LINIER: below exception
0 : ANGIER: exception
+ 1 : HEIRIER: above exception
+ 2 : VOLIER: above acception
+ 8 : QUALIER: above perception

GasicPhotons, GasicElectrons, GasicIons, GasicGases, GasicLiquids, GasicSolids, GasicMolecules

5. WHY: Mouth: Frontal Lobe: Ingestion of Matter

+ 8 , + 2 , + 1 , 0 , – 1 , – 2 , – 8

Standard prefixes with root AEST for Liquids or “Sea” which is interesting because it means that the Sea is the surface of the water.

– 8 : WEAVIEST: below perception
– 2 : POINTIEST: below acception
– 1 : LINIEST: below exception
0 : ANGIEST: exception
+ 1 : HEIRIEST: above exception
+ 2 : VOLIEST: above acception
+ 8 : QUALIEST: above perception

LiquidicPhotons, LiquidicElectons, LiquidicIons, LiquidicGases, LiquidicLiquids, LiquiidicSolids, LiquidicMolecules

6. HOW: Body: Cerebellum: Deduction of Matter

+ 8 , + 2 , + 1 , 0 , – 1 , – 2 , – 8

Standard prefixes with root TER for Liquids or “Earth” because it means that the creators of the word Earth meant “water”.

– 8 : WEAVITER: below perception
– 2 : POINITER: below acception
– 1 : LINITER: below exception
0 : ANGITER: exception
+ 1 : HEIRITER: above exception
+ 2 : VOLITER: above acception
+ 8 : QUALITER: above perception

SolidicPhotons,  SolidicElectons, SolidicIons, SolidicGases, SolidicLiquids, SolidicSolidsSolidic, Molecules

HOW MUCH: Gut: brain region unknown

+ 8 , + 2 , + 1 , 0 , – 1 , – 2 , – 8

Standard prefixes with root DUCT for Counting which is interesting because this involves the digestive process.  Molecule literally means “soft stone”.  Another word for dung.

– 8 : WEAVIDUCT: below perception
– 2 : POINTIDUCT: below acception
– 1 : LINIDUCT: below exception
0 : ANGIDUCT: exception
+ 1 : HEIRIDUCT above exception
+ 2 : VOLIDUCT: above acception
+ 8 : QUALIDUCT: above perception

MoleculicPhotons, MoleculicElectrons, MoleculicIons, MoleculicGases, MoleculicLiquids, MoleculicSolids, MoleculicMolecules.

Note: The seven International System Units are:

– 8 : WEAVE: below perception: Candela
– 2 : POINT: below acception: Ampere
– 1 : LINE: below exception: Kelvin
0 : ANG: exception: Metre
+ 1 : HEIRabove exception: Second
+ 2 : VOL: above acception: Kilogram
+ 8 : QUAL: above perception: Mole

I posted all of the above, because I believe that classification is underrated. If we spent more time thinking about the aesthetics of our classification language, which is presently total crap, we might make more discoveries.

How much do we conceal from ourselves because we deceive ourselves into thinkng a dogmatic classification system won’t bear fruit.

Have you ever seen this guy?


He beat his brains out letting the data talk to him and came up with this:

Periodic Table

When Dmitri Mendeleev created this table to describe periodic behaviour of the elements, many of the elements had not been discovered. However, the table projected what the properties of those elements would be making the search much easier.  Dmitri also was very good at making Vodka.

As I have discussed there are Satrice, Fratrice, Patrice, Natrice, Matrice, Sortrice and Datrice networks.  Each of them classify in different ways.  Understanding these networks and their classification are the road to new discoveries.  Networks are classification systems.

I just saw this in the New York Times:


It is called a “Knowledge Map”.  It is a plot of the link clicking behaviour of a scientific community.  Not what they say is important, but where they are going that they think is important.  From this information it may be possible to reorganize knowledge to make it more accessible to everyone.

And that is what we are all here for getting and giving access.

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The Zen of Systems and Networks


My own work with Enterprise Frameworks and Networks has led me to come up with the following table.  It describes the Nodes and Links in a Complete System Network.  I am saying that the Nodes representing Goals, People, Time, Locations, Code, Data, Qualities and Quantities can all be represented as Scale-free Networks and that each of these Node Networks require only one datatype.  I am also saying that there are only three types of links in networks: recursive links within a set, multiple links between sets, single links between sets.  I know of no case where this has been attempted in the manner I am attempting to represent it.


If you have been following my blog you are aware that I have been struggling for a long time to come up with a framework and a clean terminological set to describe systems.  I think I have come one step closer to that goal today.  The table above describes a Fact composed of eight Nodes (first white row illustrating entities) and the Links (last three white rows illustrating recursive, multiple and singular relationships) for each of the System Networks (Interrogative columns).  One of the interesting aspects of this System Network Model is every Fact is composed of a Unique Set of all eight Nodes.  However, all the Nodes in one Fact do not have to have Links to all the Nodes in another Fact.  Each Node within a Fact is independent regarding its Links.  Therefore you have a single set of System Facts with each Fact containing a single set of Interrogative Nodes each connected by their respective Link Networks.

I have recently been writing with the intent to challenge centrism on any one of these networks and advocate a more integrated view. I still remember dealing with data centrism, event centrism, user centrism, goal centrism, program centrism and schedule centrism over the course of my career. All of them have a role to play. My insight into all of these Nouns being Linked by Verbs in only three ways required me to look at all of the Enterprise Architectures and disengtangle the Nouns, Links and Verbs from the reasoning and representations that extend back beyond computing itself.

The Data Model below is a hybrid of Relational Models and Dimensional Models.  I call this an Associational Model.  It is using Relational Architecture to represent it.  However, I think that an alternate Entity-Attribute-Value (EAV) architecture called the Associative Model of Data would be better suited to the task.  I am using relational representation as I am still trying to communicate with a community only familiar with Relational technology.

The first thing to note about this model is Links are represented by Associations.  Associations link two Nouns using a Verb.  What is interesting about this model is every Verb, Association, Noun and Fact is unique.  The vertical connections are Many to Many relationships which allow two vertically adjacent Verbs, Associations or Nouns to have multiple unique relationships between each other.  What this means is there are no integrity problems (duplicate values) as the system network would enforce uniqueness.


The premise of this model is that the Nodes are not dimensions at all.  I am rejecting the traditional concept of dimensionality instead I am saying that there are three dimensions of Links: recursive, multiple and singular.  All we perceive are Facts, Nodes and the Links between them.

So you could come away with the following Zen koan:

entity without entity,

source without source,

path without path,

target without target,

size without size,

dimension without dimension.

Databases: Structured Associative Model


For years now I have been struggling with Relational DBMS technology and Associative DBMS technology attempting to get them to do what I want.  In my first efforts, Relational models were structurally restrictive, Dimensional models were unable to grow organically, EAV models are incompatible with relational architecture.  I came upon Simon Williams Associative Model of Data and although enthralled with its potential I found it too had limitations.  It was semi-structured and allowed for too much flexibility.  25 years in Information Technology had taught me that there was a single standard classification system for setting up databases not a plethora of ontologies.  I was determined to find the theoretical structure and was not concerned with hardware limitations, database architecture, abilties of current query languages or any other constraints.

The Associative Model of Data had made the difference in liberating me from Relational and Dimensional thinking.  A traditional ERD of the Associative Model of Data I at first thought would look like the following:


Basically what you have is a Schema composed of Nodes with Node Associations through Verbs and Associations with Nodes Attributions through Verbs. The range of Node Entities, Verb Entities, Association Entities and Attribution Entities are endless.  As well the population of the Schema has an unlimited dataset of natural key values.  I have been challenged by Relational database specialists and SQL experts regarding the viability of this model within current limitations, however their arguments are irrelevant.  What is important is the logical validity of the model, not the physical validity.

After receiving the criticism I decided to revisit the model in order to simplify it.  I went over Simon William’s explanations of his model and its application and found I could reduce it to the following:


This was profoundly simpler and better reflected the Associative Model of Data’s Architecture.  But even with this simpler architecture I was not satisfied.  I felt that the Associatve Model although giving the benefit of explicitly defining the associations was a tabula rasa.  Research has shown that tabula rasa’s are contrary to the behavior of the finite physical universe.  There is an intermediate level of nature and nuture.  And this is what I sought to model.


When I first encountered the Zachman Framework, something about it struck me in a very profound way.  I could see there was something fundamental in its description of systems, however I felt that the metaphors that John Zachman used were wrong because they themselves lacked a fundamental simplicity.  The consequences of this were that those who studied under Zachman ultimately could not agree on what he was talking about.  Also the “disciplines” that Zachman’s Framework generated were continually reinventing the wheel.  Zachman had created a world of vertical and horizontal stovepipes.  To further the confusion Zachman refused to conceive of a methodology based upon his framework.  Consequently, there was no way to determine what the priorities were in creating a system.  I call this the Zachman Clusterfuck.

Zachman’s work spawned years of work for me.  I could see that systems had a fundamental structure, but I could not agree with Zachman.  Focuses and Perspectives were useless terms.  The construction metaphor was useless.  I read anything I could get my hands on dealing with systems, methodologies, modeling, networks and a broad range of other literature across the disciplines.  Out of this came a set of conclusions:

  1. There were a fundamental set of Noun Entities
  2. There were a fundamental set of Verb Entities
  3. There were a fundamental set of Association Entities
  4. There was a clear order in which the Nouns were addressed
  5. There was a clear order in which the Verbs were executed
  6. The structure was fractal
  7. The content was a scale-free network

I made some attempts at creating the vocabulary and experimented with this new Structured Thinking Language.  However, the real break came when I worked with John Boyd’s OODA Loop:


The OODA Loop revealed a governing structure for the methodology and guided my way into the following hybrid relational/dimensional/associational model I call the Structured Associative Model of Data:


One of the key things this model demonstrates is the sequence followed by the OODA Loop.  Starting from the top, each dimension set spawns the next.  Choices are created from the dimensions.  There is no centrism to this model which is an inherent flaw in Service Oriented Architecture (SOA), Event based architecture, Data centric architecture, Goal-Directed Design, Rule based systems among others.  The stove pipes of Focuses and Pespectives disappear by reasserting a clear order of priorities and dependencies for achieving success.  The model also supports bottom up inductive as well as top down deductive sequencing.  This will make the system able to reconfigure to handle exceptions.

Some of the things I have learned in designing this model include the realization that unit defines datatype and that all measures are variable character string text.  This is because any displayed value is only a symbolic representation of the actual quantity.  If operations are to be performed on measures they are converted to the correct type as part of the operation.  I also recognized that Unit was necessary to define the scale and scalability of the system.  Further, it became apparent that analog calculations should not be practiced.  Every value should be treated as discrete and aggregated.

Another aspect of this system is the inclusion of currency and amount.  I have been critical of Zachman and academics for their hypocrisy regarding the economics of systems.  All systems have a cost and a benefit and they are measurable in currency.  Contrary to the reasoning of the majority, every decision is ultimately economic.

Tim Brown of IDEO has coined the term “Design Thinking” and has been toying with the concept for some time.  Many designers dwell on the two dimensional concept of divergence and convergence as modes of thought.  If we look at my model, divergence is the creation of choice while convergence is selection of choice.  There is no alteration or deletion of choice in my model as history is preserved.

Now what you have is a unencumbered framework with a clear methodological sequence.


Welcome to the Cognitary Universe.

Universe: Interrogative Spaces


In my previous post I gave thought to Tim Brown of IDEO’s “design thinking”, Clayton Christensen’s “Innovator’s Dilemma”, Malcolm Gladwell’s “Tipping Point”, and Buckminster Fuller’s “Synergetics” concepts.  What emerged was the above Czerepak Framework.  My claim is this framework is fundamental to designing a system.

The thing that the above table shows is interaction within what I am now going to call the “Interrogative Spaces”: HowSpace, WhatSpace, WhySpace, WhoSpace, WhenSpace, WhereSpace, HowMuchSpace, HowManySpace.  Each ellipse I call a “vortice”.  The Interrogative Spaces are composed of one or more vortices.  The Framework above shows how Spaces are composed within the Interrogatives,  but what about interactions between the Interrogative Spaces?   A good example is speed or velocity.  Speed is the intersection of WhenSpace and WhereSpace:

v = r / t

Where v is velocity, r is radius and t is time.

If you are increasing Speed, which is acceleration, you have one dimension of WhereSpace and two dimensions of WhenSpace:

a = r / t’ * t”

Where a is acceleration, r is radius, t’ is the first clock and t” is the second clock.  You cannot measure acceleration with one clock. This uniqueness of every vortice applies to all the Interrogative Spaces and all inter-relationships between all of the Spaces.  .

Another way to look at the Interrogative Spaces is as sets and subsets.  The first row are the complete Space vortice sets.  The second row are the first Space vortice subsets.  The third row is the intersect between the row two and row three Space vortice subsets. And the fourth row are the intersects between the row two and row three and row four Space vortice subsets.

I do not believe that anything is constant.  Not the speed of light, not gravity, not cosmology.  Every intersection of dimensions creates a vortex in Universe and every one is unique.  We are simply unable to measure and manage the uniqueness of everything, therefore we make generalizations which create models that can always be falsified.

Systema: Operation, Tactic, Strategy




Systema: The Six Relationships

For years I have been thinking that there are only four relationships in data modeling:

  1. Many to Many
  2. One to Many
  3. One to One
  4. Recursive

At least that’s what the books seemed to say. However I have been reconsidering since I began exploring the Zachman Framework on my own. It has become apparent to me through many practical applications that the textbooks are not always right. Below are the six basic data modeling relationships:

As you can see there are three cursive and three recursive relationships. The cursive relationships are between two separate entities. The recursive relationships are between an entity and itself. Restating them, they are:

  1. Cursive Many to Many
  2. Cursive One to Many
  3. Cursive One to One
  4. Recursive Many to Many
  5. Recursive One to Many
  6. Recursive One to One

Many to many relationships are resolved as illustrated below:

How does this fit into the Zachman Framework? Let’s examine the framework as I illustrate it below:

As you can see relationships each serve a purpose. Concepts are associations between intstances of differing entities. Contexts are one to many relationships between instances of differing entities. Logics are one to one relationships between instances of differing entities. Physics are associations between instances of the same entity. Spherics are one to many relationships between instances of the same entity. Episodics are one to one relationships between instances of the same entity.

Another way to consider this diagram is the first three relationships involve attributes, while the second three relationships involve domains.