Metadata Recycling into Graph Data Models


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Metadata Recycling into Graph Data Models

The Guide to Data Model Recycling using Neo4j

About the Book

What if the database died some years ago, but you still have an old UML or XML file with the model in it. And you need to model the same domain for graph data bases? This is one of the scenarios this book intends to help you with.

There are 70+ ERD-supporting tools and 50+ UML tools, many of which are also used for data modeling. That translates to a lot of data models. Can they be "recycled", also in agile projects? There must be hundreds of thousands of good, reusable data models! Why waste such a large resource of business metadata?

Much similar to data science we have a need to (call it "Metadata Science") read, transform, scope / reduce / enhance and adapt data models to modern database technologies. Not least graph databases, if you ask me.

This book explains how to do that. Cypher®-scripts are included for Concept Maps (CmapTools®), CSDL, XML Schemas, StarUML v1 and UML® via various XMI® formats. The most recent addition is FileMaker® data models.

The book also explains how to optionallay build a simple graph-based metadata repository, which has full lineage back to the source data model, and it supports identities, uniqueness, mandatory fields and basic datatypes.

Cypher-scripts for repository handling are indeed also part of the book (under a MIT license).

We suggest a choice of two approaches:

  • Fast Track Data Models (agile)
  • Super Data Models (crafted, using the repository.

Looking for a graph database modeling tool? Now, everything you need for recycling of data models is available inside this book. 50+ scripts (and growing) plus the repository model providing data lineage.

Any inquiries about this book and the scripts can be sent to @VizDataModeler (info at graphdatamodeling dot com).

About the Author

Thomas Frisendal
Thomas Frisendal

Thomas Frisendal is an experienced data guy with more than 30 years on the IT vendor side and as an independent consultant. He has worked with databases and data modeling since the late 70s; since 1995 primarily on data warehouse projects. He has a strong urge to visualize everything as graphs - even datamodels! He excels in the art of turning data into information and knowledge. His approach to information-driven analysis and design is "New Nordic" in the sense that it represents the traditional Nordic values such as superior quality, functionality, reliability and innovation by new ways of communicating the structure and meaning of the business context.

Thomas is an active writer and speaker.

He has previously published:

Design Thinking Business Analysis: Business Concept Mapping Applied, Springer, 2012 and

Graph Data Modeling for NoSQL and SQL: Visualize Structure and Meaning, Technics Publications, 2017

Visual Design of GraphQL Data, first on Leanpub then on Apress 2018

He is blogging at Dataversity.

Thomas lives in Copenhagen, close to the Airport.

Table of Contents

  • Introduction
  • Acknowledgements
  • Overview of the Value Proposition of Recycling Metadata
    • Concerns Drive Full Scale Data Modeling
    • Full Scale Data Architecture
  • The Atoms and Molecules of Data Models
    • Entity-Relationship Modeling
    • Relational and SQL
    • Object Orientation and UML®
    • Graph Data Models
    • Object Role Modeling (a.k.a. Fact Modeling)
    • The Key Things
    • The Universal Constituent Parts of Data Models
  • The Process of Generating Data Models
    • The Mechanics
    • Designing a Flexible Approach
  • Super Data Models
    • Load & Transform
    • Subset to get the Solution Model
    • Extend the Solution Model
    • Transform to get a Physical Model
    • Optimize the Database
  • Fast Track Data Models
    • Load & Transform
    • Auto-generate a Physical Model
    • Optimize the Database
  • Build Your Own Metadata Repository
    • The Repository on One Page
    • Concept Models
    • Solution Models
    • Physical Models
    • Lineage in the repository
    • Metadata details
  • Concept Maps (CmapTools®) as Graph Data Models
    • About Concept Maps
    • Load & Transform Concept Maps
    • The Cookbook for Concept Maps
    • From Concept Model to Super Data Model or Fast Track?
  • Conceptual Schemas in OData CSDL Format
    • About CDSL, Common Schema Definition Language from OASIS
    • Looking at the CSDL file
    • Building the Concept Model from the CSDL Model
  • FileMaker® Database Report in XML
    • About FileMaker® and XML
    • Looking at the XML-file
    • Final adjustments
  • XML Schemas from the W3C
    • About XML Schemas
    • Looking at the XSD (XML) file
    • Building the Concept Model from the XSD Model
  • StarUML ERD Models
    • About StarUML
    • Looking at the UML (XML) file
    • Building the Concept Model from the UML® ERD Model
  • UML® as XMI® the Eclipse UML 2.0 Way
    • About UML® and XMI® and Eclipse UML
    • The example: A Github Project about Carparks
    • Looking at the XMI®-file
    • Building the concept model from the UML® model
  • UML® as XMI® the EA Way
    • About UML® and XMI® and EA
    • The example: Open, Public Data
    • Looking at the XMI®-file
    • Building the concept model from the UML® model
  • Addendum: Improving Metadata Quality
    • The Business is King
    • Data Names Matter
    • Finding Standard Data Structures
    • Establishing Identity and Uniqueness
    • Presenting the Business Flow
    • Presenting the Keys
    • Presenting State Changes
    • Presenting Versions of Data
    • Housekeeping Proper
    • Scalar Data Types
    • Time Zones
    • Design is Decisions
    • Which Objects and Which Relationships?
    • Presenting Relationships and Missing References
    • Presenting the Right Level of Detail
    • Good Relationships
    • Identity, Uniqueness and Keys Revisited
    • Missing Information
  • Other books by Thomas Frisendal

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