R3DM in brief

Any information Resource can be explicitly defined as a Binary Information Resource BIR or Term Information Resource TIR and it may have one or more References, Representations and Realizations hence the name of the new data model (R3DM). Think about resources as nodes in a graph, each resource is represented with a node. There are nodes that represent digital assets or concepts that exist in the digital world (computer/web) such as files and web documents, and there are nodes that represent things on our world such as persons, objects, and abstract entities.

Representations and Realizations are referenced differently. For example, take the “Programming Language” concept. You may use the “Wikipedia” reference work, see (REF), to check for a definition or do the same with the “Free Dictionary Vocabulary”. In general you may have multiple references for the same entity. On the other hand once you reach at the realization stage, you have access of the value of the specific representation. In that case the URL address of the web/computer resource (aka realization) serves as the primary mechanism to retrieve or update that value. It is exactly the same analogy with the use of variables in computer programming to access the content of a memory storage and manipulate the value there.

Examples in TMDM

The table below shows that for an instance of type PERSON (TIR), we may have several observations of properties of type AGE and HEIGHT. For each one of these observations we can have one or more realizations.

Fig. 1 -

Example 1

Athanassios (p1) is 44 years old and his first name in Greek is Αθανάσιος

Occurrences with scope

  • {p1, AGE, “44”} /YEAR /INTEGER /PRESENT /NUMERIC /LITERAL
  • {p1, AGE, “Athanassios is 44 years old”} /STRING /PRESENT /TEXT /ENGLISH /LITERAL
  • {p1, AGE, “== Athanassios is 44 years old”} /STRING /PRESENT /WIKI /ENGLISH /LITERAL

Occurrences without scope

  • hasProp(p1:PERSON, a1:AGE)
    • {a1, VAL, “44”}
    • {a1, DTYPE,”INTEGER”}
    • {a1, UNIT, “YEAR”}
    • {a1, TIME, “PRESENT”}

Associations ONLY

  • hasProp (p1:AGENT, a1:AGE)
    • hasReal (a1:AGE, INT:DTYPE, val03:VAL, NOW:TIME, YR:UNIT)
    • hasReal (a1:AGE, STR:DTYPE, val04:VAL, NOW:TIME, HTML:ENC, EN:LANG)
  • hasProp (p1:AGENT, n1:FNAM)
    • hasReal (n1:FNAM, STR:DTYPE, val09:VAL, TXT:ENC, GR:LANG)
    • hasReal (n1:FNAM, STR:DTYPE, val00:VAL, TXT:ENC, EN:LANG)

Fig. 2 - Visualization of Example 1 in Wandora application using ONLY associations

Example 2

Athanassios (p2) was 1 m high at the age of four

Occurrences with scope

  • {p1, AGE, “4”} /YEAR /INTEGER /PAST /NUMERIC /LITERAL
  • {p1, HEIGHT, “1”} /m /INTEGER /NUMERIC /LITERAL

Associations ONLY

  • hasProp (p1:AGENT, a2:AGE, h1:HEIGHT)
    • hasReal (a2:AGE, INT:DTYPE, val07:VAL, PAST:TIME, YR:UNIT)
    • hasReal (a2:AGE, TXT:DTYPE, val08:VAL, PAST:TIME, STR:ENC, EN:LANG)
    • hasReal (h1:HEIGHT, INT:DTYPE, val05:VAL, cm:UNIT)
    • hasReal (h1:HEIGHT, TXT:DTYPE, val06:VAL, STR:ENC, EN:LANG)

Fig. 3 - Visualization of Example 2 in Wandora application using ONLY associations

Example 3

Athanassios (p1) and Aki (p2) are now taller than 1m

Associations ONLY

  • hasProp(p1:AGENT, h2:HEIGHT)
  • hasProp(p2:AGENT, h2:HEIGHT)
    • hasReal (h2:HEIGHT, INT:DTYPE, val01:VAL, NOW:TIME, cm:UNIT)
    • hasReal (h2:HEIGHT, STR:DTYPE, val02:VAL, NOW:TIME, TXT:ENC, EN:LANG)

Fig. 4 - Visualization of Example 3 in Wandora application using ONLY associations

Comparison of R3DM with TMDM

  1. Granularity TMDM: “Everything is a Topic” R3DM: “Everything is a Resource”

  2. Abstraction levels and units

There are three abstraction levels united in one

Resource = Reference + Representation + Realization

and respectively define basic information resource (IR) units at each level

Term IR (TIR) = Reference IR (RIR) + Signal IR (SIR) + Datum IR (DIR)

In TMDM you have Topic and Topic characteristics where Topic name, Topic Occurrence, Topic Role = Signal Information Resource (SIR)

  1. Address (Referencing)

In TMDM there are addressable and non-addressable subjects. This IS THE point of major confusion in many models. In R3DM addressing is dependent on the abstraction level.

In DIR the content of the datum is accessed by reference, ONLY. You MUST know the subscript of the multidimensional array. In R3DM everything is stored in a multidimensional array and there are projections in every other kind of data structure including Tables in RDBMS, Documents and Graph in NoSQL.

In RIR addressing means referencing in a broader sense and that includes modelling of many concepts here, context, scope (see TDMD), and provenance.

In SIR it is about construction of Signs (e.g. identifiers, names, labels, etc….)

  1. Relationships (R3DM) - Associations (TMDM)

There is another hot topic that of Relations-Relationships again the same tactics are followed like addressing. It depends on the abstraction level we are discussing

For example in TIR we have relationships among Terms and in DIR we are discussing about data structures.

What I consider fundamental in both models is that everything has to be based on a common unit of information processing, i.e. Topic in TMDM vs UniformIR in R3DM. Another absolutely critical similarity is the explicit definition of an abstraction level that models concepts, i.e. subjects according to TMDM or TermIR according to R3DM. Another distant similarity can be traced at the Sign level, S2 of R3DM compared to the level of occurrences in TMDM. Needless to say that in R3DM all types of identifiers are treated equally at S2.

Cross-References

R3DM Project Posts

2018

Relational Data Model: Back to the roots
Important design and implementation principle arising from studying relational data model theory
Important design and implementation principle arising from studying relational data model theory

Build valuable relations; establish effective communications
A post that explains our philosophy and goals behind HEALIS products
A post that explains our philosophy and goals behind HEALIS products

TRIADB at Connected Data London
My speech at Connected Data London conference and demos of TRIADB implementation on Intersystems Cache DBMS
My speech at Connected Data London conference and demos of TRIADB implementation on Intersystems Cache DBMS

2017

The wizards of stored computer program and the next generation of programmers
The fundamental aspect that software pioneers have been missing when they invented new programming languages or new nosql databases
Have you noticed that what ever the model and data structure in databases we cannot escape from the fundamental principle of managing data allocation space with references, i.e. pointer based logic, memory addressing ?

Associative Semiotic Hypergraph API in Mathematica for Next-Generation BI Systems
European Wolfram Technology Conference 19-20 June 2017 in Amsterdam
My speech at European Wolfram Technology Conference 2017 about a new data modeling framework R3DM/S3DM that is implemented on top of OrientDB graph database and coded in Wolfram Mathematica

Are our old data model standards out of shape ?
An overview of critical points to consider when modeling with R3DM/S3DM
Both Topic Maps and RDF/OWL exhibit signs of aging. These signs do not indicate maturity levels but on the contrary they signal a re-examination of the data modeling, information representation problem

The three dimensions of AI and a fourth one as the key to unlock them
Comments on a review of AI by John Launchbury, special assistant to DIRO, DARPA
Although there has been significant progress with first and second generation AI systems in reasoning, learning and perceiving, abstraction has not been part of the game. The mechanism of abstraction can unify these other three processes.

Associative Data Modelling Demystified: Part 6/6
Demonstration of a new data model framework that transforms OrientDB into a HyperGraph Database
Demonstration of a new data model framework that transforms OrientDB into a HyperGraph Database

Data Modelling Topologies of a Graph Database
Definition and Classification of Graph Databases into Three Categories
The associative data graph database model is still a heavy hitter, stacking up well against property graphs and triples/quadruples. Expect a comeback.

A Quick Guide on How to Prevail in the Graph Database Arena
A brief discussion on criteria to meet a differentiation strategy for graph databases
A swift introduction to the key factors that influence the performance and unification character of graph databases

Associative Data Modeling Demystified: Part 5/6
Qlik Associative Model
Qlik's competitive advantage over other BI tools is that it manages associations in memory at the engine level and not at the application level. Every data point in every field of a table is associated with every other data point anywhere in the entire schema.

2016

Associative Data Modeling Demystified: Part 4/6
Association in RDF Data Model
In this article we will see how we can define an association in RDF and what are the differences with other data models that we analyzed in previous posts of our series

Do you Understand Many-to-Many Relationships ?
Associative entities are represented differently in various data models
It is 2016 and in my opinion the situation with associative entities has become darn confusing. Edges of a Property Graph data model are bidirectional but RDF links are unidirectional.

Associative Data Modeling Demystified: Part 3/6
Association in Property Graph Data Model
In this article, we continue our investigation with the Property Graph Data model. We discuss how a many-to-many relationship is represented and compare its structure in other data models

Associative Data Modeling Demystified: Part 2/6
Association in Topic Map Data Model
In this post, we demonstrate how Topic Map data model represents associations. In order to link the two, we continue with another SQL query from our relational database

Associative Data Modeling Demystified: Part 1/6
Relation, Relationship and Association
In this article, we introduce the concept of association from the perspective of Entity-Relationship (ER) data model and illustrate it with the modeling of a toy dataset

2015

Plerophoria vs Information
The ancient Greek origin of the word information
The ancient Greek origin of the word information

Towards a New Data Modelling Architecture
Part 2: Atomic Information Resource (AIR)
We introduce the Atomic Information Resource (AIR) unit of R3DM conceptual framework

Towards a New Data Modelling Architecture
Part1 - Relational/ER Constructs in Wolfram Language
We start with terms and constructs that most of us are familiar with from the Relational and Entity-Relationship database management systems

2014

R3DM/S3DM Illustration and Formalization
Old wiki pages and LinkedIn posts ported from neurorganon.org and linkedin.com
Old wiki pages and LinkedIn posts ported from neurorganon.org and linkedin.com

2013

R3DM in TMDM
Old wiki pages on R3DM ported from neurorganon.org, examples of R3DM in TMDM
Old wiki pages on R3DM ported from neurorganon.org, examples of R3DM in TMDM

R3DM Questions and Answers
Old wiki pages on R3DM ported from neurorganon.org
Old wiki pages on R3DM ported from neurorganon.org

URIs for Real-World Objects
The problem of information resources definition, representation and identification
The problem of information resources definition, representation and identification

2012

Ignite Athens 2012 : From WWW To GGG
My first pitch at Ignite Athens on 20th of September 2012 about Neurorganon Upper Level Ontology (NULON)
My first pitch at Ignite Athens on 20th of September 2012 about Neurorganon Upper Level Ontology (NULON)