In this demo we will discuss how to create a competitive advantage through leveraging Graph Analytics and Machine Learning technology with Visualizations, which helps many industries: gas pipeline, smart cities, energy suppliers and distributors, oil & gas supply chains, oil field asset management, and utility companies better manage complex networks and real time end to end visualization by modeling the myriad relationships and dependencies in a way that closely mirrors real life scenarios.
Building financial crimes software for expert users requires understanding of the specific needs as well as tasks and roles in a larger team of investigators. The goal is to create uniquely tailored solutions to those users’ needs — not create a simple report or dashboard.Learn More
How are firms integrating AI and what are some real-world insights and examples of how they turned an idea into an impactful AI solution? Our esteemed panel of industry experts from Morningstar, Wayfair, Reuters and Expero will share perspectives from implementing AI across finance, media and e-commerce.
This session will focus on how new ML and graph analytics work together with simple deep link visualization to reduce false positives by 60%, increase detection accuracy by 70% and improve overall team transparency and productivity by more than 80% to allow real time alerting to avoid sanctions and fines
Neo4j is the most popular graph data store available today. It leverages graph technologies to help build modern high-performing applications, but it does not have any native multi-tenant support. However, you may have decided to build out your multi-tenant application and that Neo4j is the right graph data store to fit your needs. In any multi-tenant system, the trick (from a data-store side) really comes down to how to isolate one tenant’s data (physically or logically) from another tenant’s.
“Jeez, I’m out of it for a little while, everyone gets delusions of grandeur!” That’s what Han Solo said after being frozen in carbonite. I’ve been solving data problems for customers the last year and a half and am now getting back in graph DBMSs. We took a nice look at Titan last week, can’t wait to play with that some more. I’m going to give a bit of the same to Neo4j. All of this as prep for my talk at GraphDay 2016 in Austin, TX.