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Thanks! Work I did was with Neo4j, which a reasonably technical person can learn in a few days using their movie-db tutorials, where once you have a clear idea of what the categories are in your data and what is a top level "thing" vs. what an attribute of a thing is, you can get correlations. (persons, orgs, parties, IP addrs, and maybe events are the things, where addresses, dates, operating systems, contact info, are attributes. Relationships are things like "comprises", "owns", "is-located", "employs", "pays/funds", "is-member-of" etc. you need to start thinking in relationships) What annoys me about other graphdb's is they want to load your head with a complete graph theoretic framework before you can be useful, where cypher/neo works more like a graph based spreadsheet you can use to think in. I have used it in different levels of govt to map managers to financial line items, to applications, corporate entities, projects, contract counterparties, platforms, techs, machines, ip addrs, vulnerabilities, etc. Developing a clear and addressable ontology of huge organizations with tens of thousands of people and devices is probably my one of my more useful skills. The main use case for graphs to me is patchy data, where you have a pile of incomplete metadata in dispirate spreadsheets and you need to find coherent paths through all of it. I won't be in touch because I know what those people are capable of, but if graphs haven't accelerated your work already, you have some really epic times ahead! |
When you say “I know what those people are capable of” do you mean something as ominous as that sounds?