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).

“Many of us ... do not think that harmony is the great goal, or unity or peacefulness, [and] actually quite like hard questions for their own sake, and enjoy ... the life of the mind. To the question of how to live, the answer is "by disagreement.” --Christopher Hitchens

Let me say, first and foremost, that as the subtitle of the book -- A REFERENCE FOR THE THINKING PRACTITIONER -- indicates, it is targeted at the minority of practitioners who think clearly, independently and critically. It should not be a surprise, then, that those not belonging to that (alas, very small) target audience don't see its practical value. As I said so many times, if my work gained mass appeal, I would wonder what I was doing wrong. This is the sad reality, whether we like it or not. In fact, to be consistent I will go one step further: I don't assume that positive reviews are any better than negative ones -- they are frequently grounded in as faulty reasoning and/or ignorance as the critiques.

Let me also make clear that I do not place all of the blame on  the individual database practitioners or users. Rather, problems are rooted in a systemic, much more profound societal and business culture that fails to instill and encourage foundation knowledge and independent, critical thinking, which not only does not reward, but actually punishes such. This is true to a degree in all societies, of course, but in the US the problem is much more acute (there can hardly be a better demonstration of the horrendous implications of this than how the election was covered, perceived and accepted by most of the press and public) [I wrote this prior to the last two elections -- I leave it to the reader to judge the steepness of the subsequent regress.]

with David McGoveran

(Title inspired by Richard Feynman)

In Parts 1 and Part 2 we provided some clarifications following a discussion on LinkedIn about our contention that, conventional wisdom notwithstanding, database relations -- distinct from mathematical relations -- are by definition not just in 1NF, but also in 5NF, as a consequence of which the RA, as currently defined for 1NF closure, produces what the industry calls "update anomalies" and, thus, is not a proper algebra. In Part 3 we used that information to debunk some leftover misunderstandings in the discussion.

We conclude in Part 4 with comments on a private exchange that followed the public one on LinkedIn regarding the difference between the McGoveran (DMG) and Date and Darwen's (TTM)
interpretations of the RDM, which can be summarized as follows:

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.

(Title inspired by Richard Feynman)

In Parts 1 and 2 we provided some clarifications following a discussion on LinkedIn about our contention that, conventional wisdom notwithstanding, database relations -- distinct from mathematical relations -- are by definition not just in 1NF, but also in 5NF, as a consequence of which the relational algebra (RA), as currently defined for 1NF closure, produces update anomalies and, thus, is not a proper algebra. In this third part we will use that information to debunk some leftover misunderstandings in the discussion.

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), 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.

Remember XML DBMS? At one point it was the fad of the day, similar to today's NoSQL or the old new "knowledge graph" -- "the future that you ignored at the peril of being left behind". As I predicted, it went the way of all fads (ODBMS, Associative DBMS, you name them) together with their "data models" that were nothing of the sort. My prediction was grounded in the same sound foundations I rely on today -- unlike the industry we are progressing it -- that fads lack and which were and still are dismissed, evidence be damned.

Here's a typical example (comments on republication in square brackets).

 with David McGoveran 

(Title inspired by Richard Feynman)

In Part 1 we explained that all database relations are, mathematically, relations, but not all relations are database relations, which are in both 1NF and 5NF and we agreed with a statement in a LinkedIn discussion ending as follows: "Update anomalies are not as big of a problem as an algebra where relations aren't closed under join". Unfortunately, update anomalies, closure, and how relational operators were defined are all interrelated and represent an even "bigger problem". Update anomalies are not "bugs", let alone irrelevant, but actually a reflection of  that much bigger problem.

In this second part we delve into that problem.

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), 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 slightly revise, break into parts, and/or add comments and/or references.

This is an email exchange with a reader responding to my third book.
(Originally posted on 06/21/2001)

“I'm presently reading your book PRACTICAL ISSUES IN DATABASE MANAGEMENT and there are a couple of points that I find a little confusing. I'll start first by saying that I have no formal database oriented education, and I'm attempting to familiarize myself with some of the underlying theories and practices, so that I can further my personal education and career prospects (but aren't we all!). My questions may sound a little bit ignorant, but that would be because I am! (Please note ignorant, not stupid!) I'll quote you directly from the book for this (possibly I'm taking you out of context or missing something important)

Chapter 3, A Matter of Identity: Keys, pg. 75: "Databases represent assertions of fact - propositions - about entities of interest in the real world. The representation must be correct - only true propositions (facts) must be represented."

Now, correct me if I'm wrong with a basic assumption here, but isn't a database simply a model of a "real world" data collection? I would've thought that the intention of a database would be to model real life effectively (and accurately) enough to provide useful data for interpretation. Now obviously this is not an easy process with complex data types, but would it even be possible to have a 100% true proposition with only atomic data types? (i.e. can a simplified model contain only facts?) In my understanding of modeling, any model that fits real life closely enough to be a good statistical representation is a usable model. e.g. Newton's Laws are accurate enough when applied on a local scale, but we need to use Einstein's model of space-time across larger scales. Wouldn't recording only "facts" (which I would presume you mean to be statements that are provable in the objective sense i.e. no interpretation, only investigation or calculation) possibly eliminate the utility of some aspects of the database? Or do we account for the interpretative aspect in the metadata or in some other way?

Essentially, I can see what you're saying, but not necessarily how you've reached the conclusion. Admittedly in an ideal world we should be able to record only facts in a database, but this is not an ideal world. As an example, in surveys we see such questions as "Are you happy with this product?" followed by a rating system of 1-5, or 'completely unhappy to completely happy'. This is an artificial enforcement of a quantitative measure on a qualitative property. How do we account for the fact that this is interpreted data and not calculated or measured?

My questions may have little relevance to database theory in general, but the concept fascinates me!”