Turning a CmapTools concept map into a graph metamodel in Neo4j.
Thomas Frisendal worked with a government client on a Knowledge Graph kind of project and being government, much of their data is public information. And, consequently, so are the data models. The physical source data model was not a good candidate as a new target data model for several reasons, mostly related to the fact that the physical model was created over decades. It contains a number of compromises because of old data integrations and so forth. So we had to refactor the thing to become a new data model.
Loading and Analyzing the Source Conceptual Data Model
The as-is open data model was documented in two ways:
Being old school I chose the UML derivative. I downloaded the file and tried to load it into a graph data base (Neo4j, https://neo4j.com ). Neo4j has a monumentally large library of awesome procedures on Cypher (it is called APOC, Cypher is the SQL of Graph Database).A few of the procedures can load XML files into the database. And that is just what it did. No settings, no nothing other than a URL to the file.
Neo4j has an ETL tool , which morphs SQL databases into graph models.
The focus among the vendors seem to be on bottom-up (building Data Catalogs, for example), and not so much on the business level. But Metadata Automation at that level will have a significant impact, once it is ready.
This is indeed the era of the emergence of “Metadata Science”. Metadata Science does similar things for metadata as Data Science does for data.
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Keywords: cloud • Clustering architecture • Concept maps • Connected Data • cypher • data model • data modeling • DDL • Graph Databases • graph query language