In order to re-invent the value chain from linear to circular and highly connected, retailers need to modernize their IT infrastructure rapidly and cost-effectively. In addition, web-based retailers must find a way to handle scale and sophistication to remain competitive.… Read more →

[As community content, this post reflects the views and opinions of the particular author and does not necessarily reflect the official stance of Neo4j.] Why Are We Doing This? Data analysis is the phenomenon of dissecting, structuring and understanding data.… Read more →

It’s never been easier for customers to comparison shop. In a matter of minutes, customers can compare prices for a specific product across a dozen stores — and all from the comfort of home. They can even compare prices and… Read more →

Now more than ever, supply chains are vast and complex. Products are often composed of different ingredients or parts that move through different vendors, and each of those parts may be composed of subparts, and the subparts may come from… Read more →

As a retailer, if you think keeping up with Amazon is expensive and time-consuming, consider the alternative: extinction. When it comes to delivery and fulfillment, Amazon is the uncontested emperor of ecommerce. Yet, their efficiency in tracking and delivering orders… Read more →

[As community content, this post reflects the views and opinions of the particular author and does not necessarily reflect the official stance of Neo4j.] This guide runs through the basic steps for importing the bitcoin blockchain into a Neo4j graph… Read more →

Summary Data profiling is a widely used methodology in the relational database world to analyse the structure, contents and metadata of a data source. Generally, data profiling consists a series of jobs executed upon the data source to collect statistics… Read more →

In 2015, analyst firm Forrester Research published a vendor landscape report on the state of graph databases. It included a few graph technology vendors, several graph use cases and described Neo4j as the “most popular graph database.” Since then, graph… Read more →

This post originally appeared on Emil Eifrem’s CEO blog on 1 December 2017. Graph technology has come a long way, and today the transformative nature of graphs is publicly visible through examples such as financial fraud detection in Panama and… Read more →

Today’s retailers face a number of complex and emerging challenges. Thanks to lower overhead and higher volume, online behemoths like Amazon can deliver products faster and at a lower price, driving smaller retailers out of business. In order to compete,… Read more →

As part of the Neo4j Graph Platform announced at GraphConnect New York last week, we are excited to announce the general availability release of the Neo4j Graph Database version 3.3. At the heart of the Graph Platform is the native… Read more →

Today at GraphConnect New York, Neo4j has announced our transformation from the provider of a graph database into the creator of a graph platform. We are making this change to address the evolving needs of customers in their deployment of… Read more →

Most people understand the problem in any large organization today is that the data is in silos. Data is disconnected, and as a result, a report from any one source is incomplete and not necessarily actionable to the extent it… Read more →

It’s almost here, folks. GraphConnect New York is just around the corner, and we’re likely to see you in NYC in just two weeks! Of course, some of you still haven’t bought your tickets to the conference yet, so head… Read more →

Often invisible to the people outside of the field, life science researchers have been quietly embracing graph databases instead of the traditional triple and relational stores. On June 21, we invited a group of life science and healthcare researchers and… Read more →

It’s fun to watch the graph database category evolve. From being a seemingly niche category a decade ago (despite the valiant efforts of the Semantic Web community) to a modest – but important – pillar of the data world as… Read more →

[As community content, this post reflects the views and opinions of the particular author and does not necessarily reflect the official stance of Neo4j.] Nearly every analyst of the retail industry would consider Amazon as the source of the declining… Read more →

Editor’s Note: This presentation was given by Jesús Barrasa at GraphConnect San Francisco in October 2016. Presentation Summary Resource Description Framework (RDF) triple stores and labeled property graphs both provide ways to explore and graphically depict connected data. But the… Read more →

In last week’s post, we discussed why designing a fully automated fake news detector is currently infeasible and introduced a semi-automated, graph-based solution which would use machine learning to work alongside human fact checkers to scalably flag and quarantine fake… Read more →

Editor’s Note: This presentation was given by Galit Gontar and Robert Edwards at GraphConnect San Francisco in October 2016: Presentation Summary As a vertically integrated company, Glidewell Laboratories needs their processes and workflows to be flexible and open to innovation.… Read more →

It’s summertime, but that doesn’t mean we’re less active building cool stuff for you to use with Neo4j. If you haven’t heard of APOC yet – dubbed “Awesome Procedures On Cypher” – it’s a Swiss Army knife of useful utilities… Read more →

Editor’s Note: This presentation was given by Aaron Wallace at GraphConnect San Francisco in October 2016. Presentation Summary Modern enterprises need to have a full, 360-view of their customers drive their bottom line. This requires the integration of data from… Read more →

Reducing the risk of money laundering presents a similar challenge to that of fraud detection when it comes to today’s financial services landscape. Firms need to know where funds come from and where they are headed, but criminals use indirection… Read more →

Identifying and stopping fraudulent activity is harder than ever for financial services organizations. Standard anti-fraud technologies — such as a deviation from normal purchasing patterns — use discrete data. This is useful for catching individual criminals acting alone, but discrete… Read more →

[As community content, this post reflects the views and opinions of the particular author and does not necessarily reflect the official stance of Neo4j.] Click here to skip straight to the JDBC driver announcement. During his opening keynote address, Emil… Read more →