Showing posts with label DM. Show all posts
Showing posts with label DM. Show all posts

Sunday, February 15, 2026

FACTS, ENTITIES, AND BUSINESS RULES



“... In ORM there is no concept of an entity record (tuple), although relational tables can be automatically generated from an ORM model (furthermore, guaranteed to be fully normalized).” --Online comment

Object Role Modeling (ORM) is a ...a fact-oriented modeling approach for specifying, transforming, and querying information at a conceptual level. Unlike [other modeling approaches] ... fact-oriented modeling is attribute-free, treating all elementary facts as relationships ... In practice, ORM data models often capture more business rules, and are easier to validate and evolve than data models in other approaches. --ORM.net

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. 

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





Friday, October 13, 2023

EVERYBODY THINK THEY KNOW FIRST NORMAL FORM, BUT NOBODY DOES



“I have read this article in an effort to boost my academic knowledge on data modeling a bit and still have no idea what this academic author wanted to say. Apparently First Normal Form (1NF) doesn't get enough respect and then proceeds to talk about Non-First Normal Form (NFNF). But what about First Normal Form (1NF) damnit.”

By sheer chance this was posted on LinkedIn just after I published my new paper The First Normal Form: A Definitive Guide.

PRACTICAL DATABASE FOUNDATIONS

FIRST NORMAL FORM

A DEFINITIVE GUIDE

(September 2023)

Fabian Pascal

 

Table of Contents

 Introduction

1.      The Normal Form

2.      The First Normal Form

3.      Domain Decomposability & Atomicity

4.      1NF & Tables

5.      SQL & 1NF

5.1.     Repeating Groups & Repeated Attributes

5.2.   Information Principle & SQL
 

Sunday, April 30, 2023

RELATIONSHIPS AND THE RDM V2 Part 3: SEMANTIC CONSTRAINTS



Note: This is a multipart re-write of a previous series that, when completed, is intended to replace it.

In Part 1 we documented the differences between mathematical and database relations (see table in Part 1). We attributed the fallacy that the RDM can express only one type of relationship -- between relations using FKs -- to the industry being unaware of the adaptation of math relations for database management. We intimated that some of the additional features of database relations express relationships other than between relations.

In Part 2 we identified the intra-group c-relationships (and the corresponding within-relation l-relationships) in our approach to conceptual modeling:

  • Properties-entities relationships

- general dependencies

  • Properties Relationships
  • Entities Relationships

- entity uniqueness
- functional dependencies (FD)
- entity supertype-subtypes relationships

and used a simple conceptual model (CM) of six entity groups to illustrate them:

Customers (cID, cname, FICO, discount)
Products (pID, pname, price)
Salesmen (sID, sname, sales, salary, commission)
Orders (oID, pID, cID, sID, date, amount)
Order Items (oID, iID, pID, quantity)

Database design is the use of a data model (DM) (here, RDM) to formalize conceptual models (CM) -- including c-relationships -- as logical models (LM) for database representation, so it must be able to convert the business rules (BR) that express those relationships in specialized natural language at the conceptual level to formal constraints in a FOPL-based data sublanguage at the logical level.

Our intention is to demonstrate that the RDM can express all c-these relationships, but we face a difficulty.

Sunday, March 19, 2023

ON PROPERTIES IN CONCEPTUAL MODELING (rm)



Note: Reader mail (rm) posts are exchanges with my readers that raise fundamental issues. I may improve language for clarity and amplify with Ed. Notes for the benefit of readers.

“Your post Understanding Conceptual vs. Data Modeling Part 1: Data Model - The RDM Is, the E/RM Isn't is well done. However, concepts and relationships can be perceived and modeled without formulating or specifying properties. Chen did that in his ER diagrams. And informally, everyone does it as a mental model every day. I suppose anyone can define conceptual modeling however they wish to.  But at its minimum and most abstract, which is what conceptual modelling is usually understood to be, it can be done without formulating or specifying properties.” --GR

Saturday, February 4, 2023

CONCEPTUAL MODELING, LOGICAL DATABASE DESIGN AND PHYSICAL IMPLEMENTATION (sms)



Note: In "Setting Matters Straight" posts I debunk online pronouncements that involve fundamentals which I first post on LinkedIn. The purpose is to induce practitioners to test their foundation knowledge against our debunking, where we explain what is correct and what is fallacious. For in-depth treatments check out the POSTS and our PAPERS, LINKS and BOOKS (or organize one of our on-site/online SEMINARS, which can be customized to specific needs). Questions and comments are welcome here and on LinkedIn.

“A conceptual data model usually just includes the main concepts (entities) required to store information and the relationships that exist between these entities. We don’t usually include any details about each piece of information. We can consider the conceptual stage as an initial model, without all the details required to create a database.

A logical data model is probably the most-used data model. It goes beyond the conceptual model; it includes entities, relationships, details on entities’ different attributes, and unique ways to identify entities (primary keys) and establish the relationships between them (foreign keys).

A physical data model is usually derived from a logical data model for a particular relational database management system (RDBMS), thus taking into account all technology-specific details. One big difference between logical and physical data models is that we now need to use table and column names rather than specifying entity and attribute names. This allows us to adapt to the limits and conventions of the desired database engine. We also provide the actual data types and constraints that allows us to store the desired information.”
--Vertabelo.com

Sunday, January 22, 2023

CONCEPTUAL BUSINESS RULES AND LOGICAL CONSTRAINTS (sms)



Note: In "Setting Matters Straight" posts I debunk online pronouncements that involve fundamentals which I first post on LinkedIn. The purpose is to induce practitioners to test their foundation knowledge against our debunking, where we explain what is correct and what is fallacious. For in-depth treatments check out the POSTS and our PAPERS, LINKS and BOOKS (or organize one of our on-site/online SEMINARS, which can be customized to specific needs). Questions and comments are welcome here and on LinkedIn.

What's right/wrong about this database picture?

“Other than constraints on cardinality, business rules are not generally represented on data models of either kind. Even in the case of business data models, the models are supposed to represent fundamental structures, while business rules represent variable constraints.”

                                                                    --TDan.com

Monday, January 2, 2023

 NEW "DATA MODELS" 5.2 (t&n)



Note: "Then & Now" (T&N) is a new version of what used to be the "Oldies but Goodies" (OBG) series. To demonstrate the superiority of a sound theoretical foundation relative to the industry's fad-driven "cookbook" practices, as well as the evolution/progress of RDM, I am re-visiting my 2000-06 debunkings, bringing them up to my with my knowledge and understanding of today. This will enable you to judge how well my arguments have held up and appreciate the increasing gap between scientific progress and the industry’s stagnation, if not outright regress.

This is a re-published series of several DBDebunk 2002 posts on Simon Wlliams' Lazy Software so-called "Associative Model of Data" (AMD), academic claims of its superiority over RDM ("The Associative Data Model Versus the Relational model") and predictions of the demise of the latter ("The decline and eventual demise of the Relational Model of Data").

  • Part 1 was an email exchange among myself (FP), Chris Date (CJD) and Lee Fesperman (LF) in reaction to Williams' claims that triggered the series.
  • Part 2 was my response to a reader's email questioning our dismissal of Williams's claims.
  • Part 3 was my email exchange with Williams where he provided his definition of a data model on which I conditioned any discussion with him and I debunked it.
  • Part 4 is my response to a reader's comments on my previous posts in the series.
  • Part 5.1 provided the background for my critique of Edward Hurley's report on Simon Williams's Lazy Software and his so-called "Associative Model of Data" (AMD).

Tuesday, December 13, 2022

NEW "DATA MODELS" 5.1 (t&n)



Note: "Then & Now" (T&N) is a new version of what used to be the "Oldies but Goodies" (OBG) series. To demonstrate the superiority of a sound theoretical foundation relative to the industry's fad-driven "cookbook" practices, as well as the evolution/progress of RDM, I am re-visiting my 2000-06 debunkings, bringing them up to my with my knowledge and understanding of today. This will enable you to judge how well my arguments have held up and appreciate the increasing gap between scientific progress and the industry’s stagnation, if not outright regress.

 

This is a re-published series of several DBDebunk 2001 exchanges about Simon Wlliams' so-called "Associative Model of Data" (AMD), academic claims of its superiority over RDM ("The Associative Data Model Versus the Relational model") and predictions of the demise of the latter ("The decline and eventual demise of the Relational Model of Data").

Part 1 was an email exchange among myself (FP), Chris Date (CJD) and Lee Fesperman (LF) in reaction to Williams' claims that started the series.
Part 2 was my response to a reader's email questioning our dismissal of Williams's claims.
Part 3 was my email exchange with Williams, where he provided his definition of a data model on which I conditioned any discussion with him and where I debunked it.
Part 4 is my response to a reader's comments on Parts 1-3.

Thursday, November 10, 2022

NEW "DATA MODELS" 4 (t&n)



Note: "Then & Now" (T&N) is a new version of what used to be the "Oldies but Goodies" (OBG) series. To demonstrate the superiority of a sound theoretical foundation relative to the industry's fad-driven "cookbook" practices, as well as the evolution/progress of RDM, I am re-visiting my 2000-06 debunkings, bringing them up to my with my knowledge and understanding of today. This will enable you to judge how well my arguments have held up and appreciate the increasing gap between scientific progress and the industry’s stagnation, if not outright regress.

This is a re-published series of several DBDebunk 2001 exchanges on Simon Wlliams' so-called "Associative Model of Data" (AMD), academic claims of its superiority over RDM ("The Associative Data Model Versus the Relational model") and predictions of the demise of the latter ("The decline and eventual demise of the Relational Model of Data").

Part 1 was an email exchange among myself (FP), Chris Date (CJD) and Lee Fesperman (LF) in reaction to Williams' claims that started the series. Part 2 was my response to a reader's email questioning our dismissal of Williams's claims. Part 3 was my email exchange with Williams where he provided his definition of a data model on which I conditioned any discussion with him and I debunked it. Part 4 is my response to a reader's comments on my previous posts in the series.

Saturday, October 29, 2022

NEW "DATA MODELS" 3 (t&n)



Note: "Then & Now" (T&N) is a new version of what used to be the "Oldies but Goodies" (OBG) series. To demonstrate the superiority of a sound theoretical foundation relative to the industry's fad-driven "cookbook" practices, as well as the evolution/progress of RDM, I am re-visiting my 2000-06 debunkings, bringing them up to my with my knowledge and understanding of today. This will enable you to judge how well my arguments have held up and appreciate the increasing gap between scientific progress and the industry’s stagnation, if not outright regress.

This is a re-published series of several DBDebunk 2001 exchanges on Simon Wlliams' so-called "Associative Model of Data" (AMD), academic claims of its superiority over RDM ("The Associative Data Model Versus the Relational model") and predictions of the demise of the latter ("The decline and eventual demise of the Relational Model of Data").

Part 1 was the email exchange among myself (FP), Chris Date (CJD) and Lee Fesperman (LF) in reaction to Williams' claims that started the series. Part 2 was my response to a reader's email questioning our dismissal of Williams's claims.  Part 3 is my email exchange with Williams: he provided his "definition" of a data model on which I conditioned any discussion with him and I proved my point by debunking it.

Sunday, October 16, 2022

NEW "DATA MODELS" 2 (t&n)



Note: "Then & Now" (t&n) is a new version of what used to be the "Oldies but Goodies" (obg) series. To demonstrate the superiority of a sound theoretical foundation relative to the industry's fad-driven "cookbook" practices, as well as the evolution/progress of RDM, I am re-visiting my 2000-06 debunkings, bringing them up to my with my knowledge and understanding of today. This will enable you to judge how well my arguments have held up and appreciate the increasing gap between scientific progress and the industry’s stagnation, if not outright regress.

This is a re-published series of several DBDebunk 2001 exchanges on Simon Wlliams' so-called "Associative Model of Data" (AMD), academic claims of its superiority over RDM ("The Associative Data Model Versus the Relational model") and predictions of the demise of the latter ("The decline and eventual demise of the Relational Model of Data").

Part 1 was the email exchange among myself (FP), Chris Date (CJD) and Lee Fesperman (LF) in reaction to Simon Williams' claims that started the series. Part 2 is my response to a reader's email questioning our dismissal of Williams's claims.  (The reader's comments are in quotes.)

Saturday, September 17, 2022

NEW "DATA MODELS" 1 (t&n)



Note: "Then & Now" (T&N) is a new version of what used to be the "Oldies but Goodies" (OBG) series. To demonstrate the superiority of a sound theoretical foundation relative to the industry's fad-driven "cookbook" practices, as well as the evolution/progress of RDM, I am re-visiting my 2000-06 debunkings, bringing them up to my with my knowledge and understanding of today. This will enable you to judge how well my arguments have held up and appreciate the increasing gap between scientific progress and the industry’s stagnation, if not outright regress.

“Codd's aim was to free programmers from having to know the physical structure of data. Our aim is to free them in addition from having to know its logical structure.”

                                                 --Simon Williams, LazySoft

This series is a re-publication of several DBDebunk 2001 posts in response to Simon Wlliams' so-called "Associative Model of Data", academic claims of superiority over RDM ("The Associative Data Model Versus the Relational model") and predictions of the demise of the latter ("The decline and eventual demise of the Relational Model of Data").

Part 1 is the email exchange among myself (FP), Chris Date (CJD) and Lee Fesperman (LF) in reaction to Williams' claims that started the whole thing.

Saturday, May 21, 2022

NO RDBMS WITHOUT RELATIONAL DOMAINS (obg)



Note: To demonstrate the correctness and stability due to a sound theoretical foundation relative to the industry's fad-driven "cookbook" practices, I am re-publishing as "Oldies But Goodies" material from the old DBDebunk.com (2000-06), Judge for yourself how well my arguments hold up and whether the industry has progressed beyond the misconceptions those arguments were intended to dispel. I may revise, break into parts, and/or add comments and/or references. You can acquire foundation knowledge by checking out our POSTS, BOOKS, PAPERS, LINKS (or, even better, organize one of our on-site SEMINARS, which can be customized to specific needs).

The following is an email exchange with a reader and DBMS designer.

ON DATA TYPES AND WHAT A DBMS IS

(originally published in 2001)

Reader:
"I would like to hear your (or Date's) opinion on The Suneido Database … it seems to me self-contradictory. They aren't typed ... so how can they define operators, or even the idea of domains. They also say they include administrative commands, which as far as I understand isn't allowed in the THIRD MANIFESTO. While they do not claim to be an implementation of the Manifesto, their claims that their database language was created by CJ Date do not sound appropriate."

 "They don't know what [domains (distinct from programming data types)] are and what their function in the RDM is. That's common for all DBMS vendors, the claims of which should be always taken with more than a grain of salt."

Friday, March 18, 2022

ENTITIES & RECORDS (sms)



 

Note: "Setting Matters Straight" is a new format: I post on LinkedIn an online Q&A involving data fundamentals to encourage readers to test their foundation knowledge, which they can then compare with our debunking here, where we confirm what is correct and correct what is fallacious (with clarifications, wherever necessary). For in-depth treatment check out the POSTS and our PAPERS, LINKS and BOOKS (or organize one of our on-site/online SEMINARS, which can be customized to specific needs).

Q: “What is the relationship between an entity and a record?”

A: “In the context of a database design, an ‘entity’ is a type or category of persons, places, things or events. It’s a collectivisation of the nouns in a system about which you wish to keep data. For example, Employee might be the name of an entity in your system. A ‘record’ is a collection of data about a specific entity, a particular person or place, an identifiable thing, or a single event. For example, Name: ‘Dave Voorhis’, StartYear: 2019, Salary: £1,398,293 might be a record of one Employee entity in your system.”

A: “Database, file, and recordset are basically the same thing. They are collections of information or data. Each database or file or recordset typically has some sort of common purpose or definition. Like a database (relational, hierarchical, etc.) of data of a business process. A File is again a collection of data such as all transactions to be posted. A recordset is also basically a file.

Entity and table are basically the same thing. While you have the grouping of all the data, and entity (logical view) and a table (physical view) are the same. As Dave said, it is a logical grouping of a specific piece of data.

File, recordset, record, row or line are basically the same. A .csv file is a grouping of records. A file is a grouping of records. A row is an individual grouping of data from a relational database.

The last is element or attribute or field. This is the individual piece of data like Transaction_Amount or First Name.”
--Quora.com
A simple and the answer oversimplifies. But things seem simple only in the absence of foundation knowledge. Practitioners use different terms for the same thing, or the same word for different things, but that must be corrected, not accepted or validated.

Saturday, February 19, 2022

NO UNDERSTANDING WITHOUT FOUNDATION KNOWLEDGE PART 6: DEBUNKING AN ONLINE EXCHANGE 5 (obg)



Note: To demonstrate the correctness and stability offered by a sound theoretical foundation (relative to the industry's fad-driven "cookbook" practices), I am re-publishing as "Oldies But Goodies" material from the old (2000-06) DBDebunk.com, so that you can judge for yourself how well my arguments hold up and whether the industry has progressed beyond the misconceptions those arguments were intended to dispel. I may revise, break into parts, and/or add comments and/or references, which I enclose in square brackets).

A 2001 review of my third book triggered an exchange on SlashDot. This six-part series comprises my debunking at the time of both the review and the exchange in the chronological (slightly out of the)  order of the original publication.
Part 1: Clarifications on a Review of My Book Part 1 @DBDebunk.com
Part 2: Slashing a SlashDot Exchange Part 1 @DBAzine.com
Part 3: Slashing a SlashDot Exchange Part 2 @DBAzine.com
Part 4: Slashing a SlashDot Exchange Part 3 @DBAzine.com
Part 5: Slashing a SlashDot Exchange Part 4 @DBAzine.com
Part 6: Clarifications on a Review of My Book Part 2 @DBDebunk.com

CLARIFICATIONS ON A DISCUSSION OF MY BOOK PART 2

(originally posted 2/21/01)

In Part 1 debunked a review of my book @Slashdot.Org. In parts 2-5 I tackled the discussion generated there by the review. In this last part I focus on the discussion of data hierarchies covered in chapter 7 of my book [the in-vogue re-emergent graph fad].

“Chapter 7 discusses data hierarchies and trees. In a nutshell: there are no trees in SQL. The author is distressed by this. Given that a foreign key is basically a pointer, you can store trees in databases, it might not be pretty and there may not be easy way to read them and it might not be a good thing to do - but if you feel the need then get right in there. Of course I could be totally wrong about this.”
Confusing keys with pointers is one of the major errors many practitioners make ]. One intentional core advantage of the RDM is precisely that it prohibits pointers -- both physical and, as in object-orientation, logical. Exposing pointers to users has caused many unnecessary problems and complications, but offered no benefit (Don't Mix Pointers and Relations and Don't Mix Pointers and Relations - Please! in Date's RELATIONAL DATABASE WRITINGS 1994-1997). There is an easy way to demonstrate that relational keys are not, like object IDs (OID), pointers, but values: they represent uniquely identifying names/attributes of rel world entities. Pointers are system-generated internals and have no real world counterpart. The desirability of a data model that produces logical models that are faithful representations of the real world, without adding artifacts of their own. Indeed, as Date points out in Why The Object Model' is Not a Data Model in his above-mentioned book, the fact that "in the object world all the references to objects are by means of their corresponding OIDs explains why -- as is well known -- OO systems typically provide (a) two different equality comparison operators, equal OID vs. equal value and (b) two different assignment operators, assign OID vs. assign value.  Note the added complication -- what is the benefit?

Sunday, December 5, 2021

HOW NOT TO EXPLAIN THE RELATIONAL MODEL (tyfk)



Note: Each "Test Your Foundation Knowledge" post presents one or more misconceptions about data fundamentals. To test your knowledge, first try to detect them, then proceed to read our debunking, reflecting the current understanding of the RDM, distinct from whatever has passed for it in the industry to date. If there isn't a match, you can review references -- reflecting the current understanding of the RDM, distinct from whatever has passed for it in the industry to date -- which explain and correct the misconceptions. You can acquire further knowledge by checking out our POSTS, BOOKS, PAPERS, LINKS (or, better, organize one of our on-site SEMINARS, which can be customized to specific needs).

“The key idea is "Parent-Child" relationship. Entities ~ Relations ~ Tables (tilde stands for "more or less like"). Concept of a Table resonates with most of the people just as everybody intuitively grasps a concept of "rows and columns” but might struggle with "tuples and attributes". Explain relations and relationships, 1:1, 1:N, N:N etc. Explain rationale for this way of collecting and storing data, touch upon data normalization, and tell a few anecdotes about cost of storage back in 1970 and Y2K problem it have caused; add that we have inadvertently created Y10K problem while fixing it (not exactly true but not wrong either). Show an ERD diagram, trace the relationships, introduce SQL, maybe run a few simple SELECT queries to help your listeners visualize it, including equijoin and ORDER BY. Save other JOIN types, data types and other, more advanced topics, and for the next encounter.”
--Quora.com

 An excellent example that validates my claim of lack of foundation knowledge in the industry: most "explainers" of RDM have acquired relational jargon, but do not know or understand it at all.

Friday, February 12, 2021

TYFK: What Is a Relational Database and Why Is It Important?



Note: Each "Test Your Foundation Knowledge" post presents one or more misconceptions about data fundamentals. To test your knowledge, first try to detect them, then proceed to read our debunking, which is based on the current understanding of the RDM, distinct from whatever has passed for it in the industry to date. If there isn't a match, you can acquire the knowledge by checking out our POSTS, BOOKS, PAPERS, LINKS (or, better, organize one of our on-site SEMINARS, which can be customized to specific needs).

“The most popular data model in DBMS is the Relational Model. It is more scientific a model than others. This model is based on first-order predicate logic and defines a table as an n-ary relation. The main highlights of this model are:
  • Data is stored in tables called relations.
  • Relations can be normalized.
  • In normalized relations, values saved are atomic values.
  • Each row in a relation contains a unique value.
  • Each column in a relation contains values from a same [sic] domain.”
--What is a relational database and why is it important, Quora.com

Friday, January 1, 2021

OBG: Database Design and Guaranteed Correctness Part 2



Note: This is a re-write of an earlier post (which now links here), to bring it into line with the current understanding of the RDM derived from McGoveran formalization and interpretation of Codd's work[1]. Reference [9] is also an important re-write and is recommended pre-requisite for this post.

Continued from Part 1

 “The term database design can be used to describe many different parts of the design of an overall database system. Principally, and most correctly, it can be thought of as the logical design of the base data structures used to store the data. In the relational model these are the tables and views ... However, the term database design could also be used to apply to the overall process of designing, not just the base data structures, but also the forms and queries used as part of the overall database application within the database management system(DBMS). The process of doing database design generally consists of a number of steps which will be carried out by the database designer. Usually, the designer must:

  • Determine the data to be stored in the database.
  • Determine the relationships between the different data elements.
  • Superimpose a logical structure upon the data on the basis of these relationships.
Within the relational model the final step above can generally be broken down into two further steps, that of determining the grouping of information within the system, generally determining what are the basic objects about which information is being stored, and then determining the relationships between these groups of information, or objects.”
--What is a Relational Database, Quora.com
There is, typically, much vagueness and confusion here and instead of debunking it makes more sense to provide a rigorous description of what database design really is: formalization of a conceptual model -- expressed as business rules -- as a logical model for representation in the database using a formal data model. If the data model is the RDM, the logical model consists of relations constrained for semantic consistency with the conceptual mode, the constraints being formalizations of the business rules.

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