GK writes:
“I just wanted to drop a note of thanks for the website, especially the latest articles on understanding data modeling, which among other things, explains very nicely the difference between the application of set theory and graph theory. It parallels in the real world with the community (set of data elements) and the individual (node in a network) and how it is easier to connect communities (RDM), but how much more complex it would be to connect individuals directly (GDM) without going through such a community connection arrangement (e.g. e-mail, postal system).”
“I'm currently working out the concept of what I call CMCs or contextual metadata connectors. I'm sure such entities will be heavily dependent upon the usage of RDM to do their job. In the project, I would like to use both approaches (RDM, GDM) due to the power of set theory and graph theory, but exactly where one should do so is so critical.”
“It's exciting to think of the endless potential for AI-based automation when one correctly leverages the underlying principles of data relationships. Since my discovery in 2004 about a much better way to approach test automation which I called data-centric (vs. the code-centric industry standard), I have found that it applies anywhere there is data, as long as one holds to a proper understanding of data and how to view it relationally.”
“What I find very surprising though is how rare it is to find in the I.T. industry a proper understanding of data, especially when viewing it relationally. It is indeed one of the most massively misunderstood aspects of the I.T. industry to this day, as your website alludes to. Rather than running away from it, RDM should be the very first course taught in any program involved in either computer science or information science. Maybe then I wouldn't always be losing people in technical conversations whenever I start talking about it. I see a diamond and they just see carbon.”
While it is nice to have one's work appreciated, the following text appears in every post and cannot be missed:
“Up to 2018, DBDebunk was maintained and kept free with the proceeds from my @AllAnalitics column. In 2018 that website was discontinued. You will not find its content anywhere else, so if you deem it useful, particularly if you are a regular reader, please back up your appreciation with concrete support -- purchase publications, or regular donations. Thank you.”
Codd was explicit about introducing the set-based RDM to relieve what he called "non-network applications" -- concerned with relationships among groups of entities -- from the complexity burden of directed graphs for network applications concerned with relationships among individual entities. But this too, like so many other aspects of his work, was missed/ignored. Witness the GDBMS revival and promotion as "superior to RDBMSs" (which are confused with SQL DBMSs), without any reference to their distinct application domains.
Furthermore, as we have often pointed out, the older generation GDBMSs were actually not grounded in graph theory, but were abstractions from industry practices, and although the current crop are improvements -- having learned from the RDM -- there is no agreed, formally well defined, theory based graph data model (GDM)[1,2]. If there is, what are -- precisely, please! -- its structure, manipulation, and integrity components?[3].
I am not familiar with CMCs, but extreme care must be exercised with respect to "using both approaches (RDM, GDM) due to the power of set theory and graph theory", to prevent the latter (based on higher logic) from defeating the purpose and advantages of the former (intentionally restricted to FOPL)[4,5].
While I do not disagree with the data-centric vs. code-centric argument, I have serious reservations -- to put it politely -- for a multiplicity of reasons to "endless potential of AI-automation", which are beyond the scope of this response.
Surprising? Since the late 80s all our writings (at the old DBDebunk, and elsewhere and at this blog; papers; books; and seminars have done nothing but document and explain the lack of knowledge and understanding of data fundamentals in the industry[6,7,8,9,10,11]. It has much to do with the destruction of education and its replacement with tool training[12,13], a component of the decadence and decline of Western civilization. The rich irony of promoting "data science", while discarding the real data science (the RDM) escapes, of course, the industry[14,15].
References
[1] Pascal, F., Graph Databases They Who Forget the Past...
[2] Pascal, F., OO/UML, and "Graph Data Models"
[3] Pascal, F., What Is a Data Model, and What It Is Not.
[4] Pascal, F., Structure, Integrity, Manipulation: How to Compare Data Models.
[5] Pascal, F., Natural, Programming, and Data Language.
[6] Pascal, F., THE DBDEBUNK GUIDE TO MISCONCEPTIONS ABOUT DATA FUNDAMENTALS - A DESK REFERENCE FOR THE THINKING DATA PROFESSIONAL AND USER (October 2016).
[7] Pascal, F., Database Management No Progress Without Data Fundamentals.
[8] Pascal, F., Industry Practice Is No Substitute for Foundation Knowledge.
[9] Pascal, F., The Cookbook Approach to Data Management.
[10] Pascal, F., Are You a Thinking Data Professional?
[11] Pascal, F., Lenin, Trotsky, Data Management, and the Tyranny of Knowledge and Reason.
[12] Pascal, F., A Note on Education vs. Training.
[13] Pascal, F., Education, Practicality and an Introductory SQL Book.
[14] Pascal, F., The Real Data Science.
[15] Understanding Relations: Tables? So What?
