QuickGraph#5 Learning a taxonomy from your tagged data

The Objective Say we have a dataset of multi-tagged items: books with multiple genres, articles with multiple topics, products with multiple categories… We want to organise logically these tags -the genres, the topics, the categories…- in a descriptive but also actionable way. A typical organisation will be hierarchical, like a taxonomy. But rather than building it […]

Neo4j is your RDF store (part 2)

As in previous posts, for those of you less familiar with the differences and similarities between RDF and the Property Graph, I recommend you watch this talk I gave at Graph Connect San Francisco in October 2016. In the previous post on this series, I showed the most basic way in which a portion of your graph […]

Graph DB + Data Virtualization = Live dashboard for fraud analysis

The scenario Retail banking: Your graph-based fraud detection system powered by Neo4j is being used as part of the controls run when processing line of credit applications or when accounts are provisioned. It’s job is to block -or at least to flag- potentially fraudulent submissions as they come into your systems. It’s also sending alarms to fraud operations analysts […]

Neo4j is your RDF store (part 1)

If you want to understand the differences and similarities between RDF and the Labeled Property Graph implemented by Neo4j, I’d recommend you watch this talk I gave at Graph Connect San Francisco in October 2016. Intro Let me start with some basics: RDF is a standard for data exchange, but it does not impose any particular way […]

QuickGraph#4 Explore your browser history in Neo4j

The dataset For this example I am going to use my browser history data. Most browsers store this data in SQLite. This means relational data, easy to access from Neo4j using the apoc.load.jdbc  stored procedure. I’m a Chrome user, and in my Mac, Chrome stores the history db at ~/Library/Application Support/Google/Chrome/Default/History There are two main tables in the […]