Resources

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How Boston Scientific Improves Manufacturing Quality Using Graph Analytics

Tracking end of line manufacturing issues to their source can be a daunting task. Boston Scientific, in partnership with GraphAware, has used the Neo4j platform to build a manufacturing quality tool that offers dramatic improvements to the time, quality, and quantity of investigations. In this talk we will review a manufacturing value stream in a graph and discuss the analysis methods available, which result in striking increases in business efficiencies, for this unique application. We will also present how the system was implemented within the existing data architecture and then scaled from a laptop investigational tool to an enterprise-grade solution with Neo4j Server.

When privacy matters! Chatbots in data-sensitive businesses

When privacy matters! A series of challenges for chatbots in data-sensitive businesses such as healthcare and finance by Christophe Willemsen

Meetup: Integration of Chatbots in Healthcare and BFSI, Dubai, 1.11.2018

How to Know What You Know: 5-Minute Interview with Dr. Alessandro Negro, Chief Scientist at GraphAware

Read the interview with our Chief Data Scientist Alessandro Negro published on Neo4j blog, where he talks about how GraphAware uses natural language processing to help companies gain a better understanding of the knowledge that is spread across their organization.

Read the interview

GraphAware Audit Module Overview & Demo

The GraphAware Audit module seamlessly and transparently captures a full audit history who, when, and how a graph was modified.

Signals from outer space

Vlasta Kus talked about the advantages of graph-based natural language processing (NLP) using a public NASA dataset as example. From his abstract: “[…] we are building a platform (from large part open-source) that integrates Neo4j and NLP (such as Named Entity Recognition, sentiment analysis, word embeddings, LDA topic extraction), and we test and develop further related features and tools, lately, for example, integrating Neo4j and Tensorflow for employing deep learning techniques (such as deep auto-encoders for automatic text summarisation).”