Need for Logical Schema

In order to effectively exchange information, a logical schema is necessary.  Why?  Because, without that logical schema you simply cannot prove to yourself or others using the information that the information represented is not "garbage".  Without a logical schema you simply cannot prove to yourself that you have represented the information correctly.  Further, without a logical schema anyone trying to make use of that represented information cannot understand how to do so and cannot verify for themselves that the information represented is not garbage.

That is the fundamental purpose of the Seattle Method which I have summarized in the document Purpose of Seattle Method.  In that document I provide 9 verification categories.  That last "Other" is a catch all where additional verification can be added.  I have combined two math related verification categories that I had separated out because of an XBRL related idiosyncrasy.  These are the seven MINIMUM verification categories that seem to be common to every area of knowledge:

  1. Physical syntax format needs to be consistent with the applicable technical specification.
  2. Mathematics logic of information represented needs to be right. (Combines two items from document)
  3. Model structure logic needs to be consistent with expectation.
  4. High level continuity logic between structures should not be contradictory or inconsistent.
  5. Type-subtype logic need to be consistent with expectations. (a.k.a. wider-narrower; general-special)
  6. Structure’s mechanics logic of each structure needs to be consistent with expectations.
  7. Information package content needs to be consistent with the expectations. (what is provided is consistent with what is deemed required to be provided)
There are no shortcuts in life. A craftsperson needs to understand their craft.

A logical system has a logical schema that articulates information about the logical theory that describes the logical system and makes sure information in the logical system is consistent with expectations.



Comments

Post a Comment

Popular posts from this blog

Relational Knowledge Graph System (RKGS)

Image
Relational Knowledge Graph System (RKGS) is an interesting idea that combines the relational paradigm and the knowledge graph paradigm.  This documentation, Why RKGS , explains the idea. This gets even more interesting with the notion of "relational AI".  RelationalAI is described as follows by the company of the same name, Relational.ai : “RelationalAI is a cloud-based relational knowledge graph management system, with state of the art probabilistic processing and declarative reasoning at scale to make developing Data Applications a superpower for your business.” This notion of knowledge graphs represented in relational databases fits into my belief that knowledge graphs have two aspects: the expression SYNTAX and the logic of what is expressed or SEMANTICS. While the LOGIC of a knowledge graph is always the same regardless of technical syntax used to express that logic; there are many, many different technical syntaxes that can be used to represent a knowledge graph. There
Read more

Getting Started with Auditchain Luca

Image
The Innovator's Dilemma points out that disruptive innovations often look like toys that are doing everything wrong. Auditchain Luca , which is part of the Auditchain Suite , is a unique approach to creating a knowledge graph .  To experience using Auditchain Luca, have a look at the Getting Started with Auditchain Luca instructions. Be sure to grab the Excel and JSON import files used for this getting started exercise.   This getting started exercise uses one of several different approaches to getting information into a tool for constructing and viewing professional quality knowledge graphs .  Additional getting started guides will be made available over time. Auditchain Luca might not make sense to you if you don't understand the paradigm shift that is occurring and if you have not shifted your mental map. For the next step in your journey, consider trying creating a report using a financial reporting scheme template . If you are struggling to figure out how to register or
Read more

Evaluating the Quality of XBRL-based Financial Reports

Image
From the time public companies started submitting XBRL-based financial reports to the U.S. Securities and Exchange Commission, I have been looking at those reports, trying to understand the XBRL-based reports, and trying to figure out how to get them right.  That information was used to figure out how to create software applications that help professional accountants get these reports right. Here is information that summarizes my testing and results which I stopped doing March 31, 2019: Analysis of the high level accounting relationships in the 10-Ks of about 5,716 financial reports  (5,063 public companies, about 89.1% got all these high level accounting relationships consistent with expectation, 623 were inconsistent and manual confirmation revealed the inconsistency was an error) Documentation that helps you understand 26 different types of errors in XBRL-based reports High quality documentation of hundreds of errors in XBRL-based reports submitted by public companies to the SEC Doc
Read more