If you have read our post Hume in Space: Monitoring Satellite Technology Markets with a ML-powered Knowledge Graph, you surely wonder: is there a way to extract relations among named entities without heavy investment? Investment in terms of time to label training dataset and to develop, train and deploy a machine learning model?
GraphTour Europe 2020 started in Amsterdam on February 4, right after the release of Neo4j 4.0, a key milestone in the graph technology landscape. At GraphAware we are very excited about the new features included in this release because they revolutionize the way we approach some common graph challenges. Our CEO, Michal Bachman spoke about this in Amsterdam in his talk “Practical Applications of Neo4j 4.0”, and he and other GraphAware experts continue to present in each of the six cities where we sponsor GraphTour. Find out what’s the next virtual event and register for free.
Neo4j has implemented very useful algorithms in order to derive insights from your graph data.
Everyone has a passion for something. Be it music, politics, sports, coffee or … pancakes. Such passion makes you strive for new information, for understanding of the current trends. Take pancakes: you might watch for new recipes on your favourite website, you might look at cooking shows or youtube videos to get more inspiration about how to serve them … but overall, you can probably handle this pretty well. It’s not like there is much room for revolutionising the pancake recipe.
GraphAware Hume helps governments in keeping their countries safe. In this 15-minute video, we demonstrate the use of Hume for contact tracing and smart quarantine in the context of the current coronavirus pandemic. Specifically, we will see how Hume can identify people at risk using actual and potential contact tracing, suggest who should be informed or quarantined, visually explain why someone is at risk, find quarantine offenders, and much more.
So, you’ve built an amazing application with your favorite framework such as Vue.js or React.js and now it’s time to build a Docker image and ship it.
Neo4j 4.0 has just been released with a key feature: graph and sub-graph access control. Access to certain labels or relationship types or properties can now be handled at the database level, resulting in developers not having to deal with complex security logic in their code, and also providing a more consistent and performant solution.
It’s been a year since I published the Graph Technology Landscape 2019 post on GraphAware’s blog. I consider this a success story because it got a lot of attention and publicity. The landscape was mentioned many times at different places; it was used by Emil Eifrem in his GraphTour and GraphConnect opening keynotes, it was displayed in conference halls, and I received many, many useful comments and feedback. I was even invited to Rik van Bruggen Graphistania Podcast to talk about it, and the episode was referred to in the Top 5 Neo4j Podcasts of 2019 blog posts as well....
Up until version 4.0, Neo4j has supported only one active database per server instance. As such, achieving multi tenancy meant that either a Neo4j instance had to be deployed per tenant, or all tenant graphs co-existed in the same database.