Jim Webber is the Chief Scientist at Neo4j working on next-generation solutions for massively scaling graph data. Prior to joining Neo4j, Jim was a Professional Services Director with ThoughtWorks where he worked on large-scale computing systems in finance and telecoms. Jim has a Ph.D. in Computing Science from the Newcastle University, UK.
Last year's NODES event concluded with the enigmatic Jim Webber – Neo4j's Chief Scientist and CTO – interviewing a series of graph devotees, users, customers, and community members. Interviewees included a risk modeling scientist, a civic tech practitioner, and a software... read more
Editor’s note: This presentation was given by Jim Webber at Connections: Knowledge Graphs for Transformation. As Neo4j's chief scientist, I have been working on a report on knowledge graphs, and one of the things that really captured my interest in writing this report was the notion of... read more
And we’re back with another installment from NODES 2021! In the third of the day’s closing keynote interviews, Jim Webber, Neo4j’s Chief Architect and CTO, sat down (virtually) with Matthias Sieber, a long-time graph devotee whose vast experience has served him across industries and... read more
At this year’s NODES event, Jim Webber – Neo4j’s Chief Architect and CTO – wrapped up the day with an enthralling series of interviews with customers and community members. His keynote showcased the stories of four different Neo4j users, each of whom had their own unique path to... read more
At this year’s NODES 2021 closing keynote, Dr. Jim Webber, Neo4j’s Chief Scientist, unearthed some of Neo4j’s most varied and vibrant community members and customers to give them a chance to tell their graph stories. As he said in his intro leading up to this session, “I've tried to... read more
Graph technology is the future. Not only do graph databases effectively store relationships between data points, but they’re also flexible in adding new kinds of relationships or adapting a data model to new business requirements. But how do companies today use graph databases to solve tough... read more
Graph technology is the future. Not only do graph databases effectively store relationships between data points, but they’re also flexible in adding new kinds of relationships or adapting a data model to new business requirements. But how do companies today use graph databases to solve tough... read more
Graph technology is the future. Not only do graph databases effectively store relationships between data points, but they’re also flexible in adding new kinds of relationships or adapting a data model to new business requirements. But how do companies today use graph databases to solve tough... read more
Graph technology is the future. Not only do graph databases effectively store relationships between data points, but they’re also flexible in adding new kinds of relationships or adapting a data model to new business requirements. But how do companies today use graph databases to solve tough... read more
Graph technology is the future. Not only do graph databases effectively store relationships between data points, but they’re also flexible in adding new kinds of relationships or adapting a data model to new business requirements. But how do companies today use graph databases to solve tough... read more
Editor’s Note: This presentation was given by Dr. Jim Webber at GraphTour Boston in 2019. Full Presentation If there’s any area of computer science that’s prone to nonsense today, it’s artificial intelligence. I'm going to walk you through some no-nonsense definitions of... read more
Graph technology is the future. Not only do graph databases effectively store relationships between data points, but they’re also flexible in adding new kinds of relationships or adapting a data model to new business requirements. But how do companies today use graph databases to solve tough... read more
Graph technology is the future. Not only do graph databases effectively store relationships between data points, but they’re also flexible in adding new kinds of relationships or adapting a data model to new business requirements. But how do companies today use graph databases to solve tough... read more
Graph technology is the future. Not only do graph databases effectively store relationships between data points, but they’re also flexible in adding new kinds of relationships or adapting a data model to new business requirements. But how do companies today use graph databases to solve tough... read more
Graph technology is the future. Not only do graph databases effectively store relationships between data points, but they’re also flexible in adding new kinds of relationships or adapting a data model to new business requirements.c But how do companies today use graph databases to solve... read more
Graph technology is the future. Not only do graph databases effectively store relationships between data points, but they’re also flexible in adding new kinds of relationships or adapting a data model to new business requirements. But how do companies today use graph databases to solve... read more
I feel particularly pleased to be able to announce the GA release of Neo4j Graph Database 4.0. I've been with the Neo4j codebase since 2009 and have to tell you: 2009 Jim Webber couldn't imagine the way 2020 Neo4j would look. “2009 me” was building a REST API (if you've ever seen... read more
It’s one thing to say that Neo4j streamlines real-time recommendations; it’s another to show you the code so you can see for yourself. In this series, we discuss how real-time recommendations support a number of different use cases, from product recommendations to logistics. Last week,... read more
Relevant, real-time recommendations drive revenue, but they are challenging to deliver. That’s because good recommendations require bringing together so much data, surfacing the relationships between all that data and delivering just the right suggestion in context and in the moment. In... read more
Effective recommendations increase revenue and drive up average order value. But delivering highly relevant, real-time recommendations requires as much context as possible. Connecting the user to the perfect recommendation is an art. In this three-part series, we explore using recommendations to... 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 it is today. But as the category has grown, we see some... read more
Hi everyone. My name is Dr. Jim Webber. I'm Neo4j's chief scientist, and I'd like to take a few minutes to talk to you about Neo4j's evolution from an engineering point of view. Our Origin Story: Overcoming Complicated SQL Queries I don't know if many people know this, but Neo4j actually... read more
Neo4j is a native graph database, which means all of the code – from the web browser to the drivers, Bolt protocol, page cache, cluster protocols, disk storage format — is optimized for graph workloads. As hardware advances come along, we’re uniquely positioned to adapt our software design to... read more
Editor’s Note: Last October at GraphConnect San Francisco, Jim Webber – Chief Scientist at Neo Technology – delivered this very sciency closing keynote on the graph database landscape. For more videos from GraphConnect SF and to register for GraphConnect Europe, check out... read more
“Big data” grows bigger every year, but today’s enterprise leaders don’t only need to manage larger volumes of data – they critically need to generate insight from their existing data. So how should CIOs and CTOs generate those insights? To paraphrase Seth Godin, businesses need to... read more