“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.”

“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.”

Fact Modeling

“... another school of modelers working with "fact modeling". Their approach is not new. It goes back to the 70's, where Eckhard Falckenberg and Sjir Nijssen started working on the approach (in parallel). Fact Modeling was known for many years as Object-Role-Modeling (ORM), and it was supported by the popular Visio diagramming tool at the time that Microsoft bought the company behind Visio. I like Nijssens name “Binary Relationship Modeling” a lot and it has been in the back of my head since the early 80's. Fact Modeling is definitely at the right level (concepts and their relationships), but it also contains all of the logic details required for formal, precise specifications. The visual syntax goes back to: Nijssen, G.M. and T.A. Halpin, Conceptual Schema and Relational Database Design — A fact oriented approach, Prentice Hall 1989.”

Object Orientation and Unified Modeling Language

“A "counter revolution" against the relational movement was attempted in the 90’s. Graphical user interfaces came to dominate and they required advanced programming environments. Functionality like inheritance, sub-typing and instantiation helped programmers combat the complexities of highly interactive user dialogs. The corresponding Data Modeling tool is the Unified Modeling Language ...”

"Entity-Relationship Model"

“One of the first formal attempts at a framework for Data Modeling was the Entity-Relationship data model paradigm proposed [in 1976] by Peter Chen. Notice that in the original Chen-style, the attributes are somewhat independent and the relationships between entities are named and carry cardinalities ("how many" participants in each end of the relationship) ... Attributes are related to their "owner" entity" in what other people called "functional dependencies".”

“... I needed to know what the constituent parts of data models really are. Across the board, all platforms, all models etc. Is there anything similar to atoms and the (chemical) bonds that enables the formation of molecules? My concerns were twofold ... I wanted a simple, DIY-style, metadata repository for storing 3-level data models -- what would the meta model of such a thing look like? -- [where] atomicity is of essence ... I took a tour (again) in the Data Modeling zone, trying to deconstruct the absolutely essential metadata, which data modelers cannot do without.”
--Thomas Friesendal, The Atoms and Molecules of Data Models, Dataversity.com

“The relational databases that emerged in the ’80s are efficient at storing and analyzing tabular data but their underlying data model makes it difficult to connect data scattered across multiple tables. The graph databases we’ve seen emerge in the recent years are designed for this purpose. Their data model is particularly well-suited to store and to organize data where connections are as important as individual data points. Connections are stored and indexed as first-class citizens, making it an interesting model for investigations in which you need to connect the dots. In this post, we review three common fraud schemes and see how a graph approach can help investigators defeat them.”

--AnalyticBridge.DataScienceCentral.com

• There is no "tabular data" (the relational data structure is the relation, which can be visualized as a table on a physical medium[2], and SQL tables are not relations);

• Analysis is not a DBMS, but an application function (while database queries, as deductions, are an important aspect of analysis, and computational functions can be added to the data sublanguage (as in SQL), the primary function of a DBMS is data management)[3];

• A data model has nothing to do with storage (storage and access methods are part of physical implementation, which determines efficiency/performance[4]).