With the aim to monitor, prevent, and predict cyber attacks on various systems and infrastructures, the cyber defence company needed a solution to ingest and connect all available data and discover threat patterns.
This presentation by Christophe Willemsen, CTO, GraphAware, guides you through security best practices for Neo4j development.
This session features Dr. Alessandro Negro, noted graph database author and Chief Scientist at GraphAware, along with Patrick Wall, Director of Product Marketing at Neo4j. During this webinar, GraphAware explores the powerful scalability features of Neo4j 4.0 in a live demo using the COVID-19 Open Research Dataset.
Graphgen aims at helping people prototyping a graph database, by providing a visual tool that ease the generation of nodes and relationships with a Cypher DSL. Many people struggle with not only creating a good graph model of their domain but also with creating sensible example data to test hypotheses or use-cases. Graphgen aims at helping people with no time but a good enough understanding of their domain model, by providing a visual dsl for data model generation which borrows heavily on Neo4j Cypher graph query language. The ascii art allows even non-technical users to write and read model descriptions/configurations as concise as plain english but formal enough to be parseable. The underlying generator combines the DSL inputs (structure, cardinalities and amount-ranges) and combines them with a comprehensive fake data generation library to create real-world-like datasets of medium/arbitrary size and complexity. Users can create their own models combining the basic building blocks of the dsl and share their data-descriptions with others with a simple link.
GraphAware: Towards Online Analytical Processing in Graph Databases
MSc. Thesis submitted for MSc. program in Computing at Imperial College London, written by GraphAware's managing director Michal Bachman.
