By Thomas Frisendal
From Karen Lopez's Foreword:
In this booklet, Thomas Frisendal increases vital questions on the continuing usefulness of conventional facts modeling notations and approaches:
- Are Entity courting Diagrams (ERDs) correct to analytical information requirements?
- Are ERDs appropriate within the new international of massive Data?
- Are ERDs nonetheless find out how to paintings with company clients to appreciate their needs?
- Are Logical and actual information versions too heavily coupled?
- Are we right in utilizing an analogous notations for speaking with company clients and developers?
- Should we refine our present notations and instruments to fulfill those new wishes, or should still we commence back from a clean page?
- What new notations and ways do we need?
- How can we use these to construct firm database systems?
Frisendal takes us throughout the background of information modeling, firm info versions and conventional modeling equipment. He issues out, fairly contentiously, the place he feels now we have long gone fallacious and in a couple of locations the place we received it correct. He then maps out the psychology of that means and context, whereas settling on very important matters approximately the place information modeling could or won't slot in enterprise modeling. the most topic of this paintings is a suggestion for a brand new exploration-driven modeling procedure and new modeling notations for company inspiration types, company strategies types, and actual info types with examples on how you can leverage these for imposing into any objective database or datastore. those new notations are in accordance with a estate graph method of modeling data.
From the author's introduction:
This publication proposes a brand new method of information modeling-one that "turns the interior out". For good over thirty years, relational modeling and normalization was once the secret. you possibly can ask that if normalization was once the reply, what was once the matter? there's something upside-down in that procedure, as we'll see during this ebook.
Data research (modeling) is far like exploration. virtually actually. the information modeler wanders round looking for constitution and content material. It calls for conception and cognitive abilities, supported by means of instinct (a mental phenomenon), that jointly make sure how good the panorama of industrial semantics is mapped.
Mapping is what we do; we discover the unknowns, draw the maps and publish the
"Here be Dragons" warnings. after all there are technical abilities concerned, and unusually, an important ones come from psychology and visualization (again conception and cognition) instead of natural mathematical skill.
Two compelling occasions make a paradigm shift in facts modeling attainable, and likewise necessary:
- The advances in utilized cognitive psychology deal with the desires for correct contextual framework and for higher verbal exchange, additionally in info modeling, and
- The swift consumption of non-relational applied sciences (Big information and NoSQL).