Over 10,000 physical typewritten documents from 1932 to 1941 had to be digitised, structured, and connected in order to create a single, centralised source of knowledge, for enabling the analysis of historical processes.
‘The amount of savings in time and effort [the search optimization] can deliver for our home offices, for our customers, is incredible.’
--Mayank Gupta, SVP for data, LPL Financial
Fraud is becoming more and more complex. Learn how graphs and Hume can help you fight and detect fraud.
An introduction to Graph-Powered Machine Learning written by our very own Dr. Alessandro Negro. This book is an extraction of 60 combined years of experience in graphs, and explains how graphs and graph databases can serve machine learning projects.
GraphAware and Neo4j experts demonstrate how you can leverage knowledge graphs to help with compliance challenges.
‘GraphAware Hume and Neo4j have significantly reduced the amount of manual effort required to keep documentation consistent and mitigate compliance risk.’
Dr. Miro Marchi and Michal Trnka explore 10 of the most useful graph entity states using Cypher to enrich entities with contextual information enabling powerful interactions.
Christophe Willemsen describes Role Based Access Control (RBAC), intra-cluster encryption and logging in Neo4j.
Luanne Misquitta shows how to use the results of a UNION Cypher query.
Luanne Misquitta explains how to produce good starting-point recommendations for whisky using Cypher that are of higher quality than those we see at our favourite online stores.
Vlasta Kůs takes us through converting a corpus of research papers through Natural Language Processing, entity (relation) extraction and graph algorithms to highly informative connected insights organized in a knowledge graph.