This is the second chapter in our blog series on GPU-accelerated graph analysis using RAPIDS cuGraph. In the first chapter, we introduced…

We have seen recently a surge in vector databases in this era of generative AI. The idea behind vector databases is to index the data with vectors that relate… | 30 comments on LinkedIn

By Paula Wojcik, Bastian Bunzeck & 1 more. This articles explores character representations in Wikidata across various language editions.

Change & Risk Require New Enterprise Tools As AI systems, bots (both digital and physical), AI leveraged smart digital identities, and IoT devices invade your enterprise, it increases the pace of change and risk. This brief article focuses on why you should be deploying TODA, EMS and graphs in your

News from eight different companies shows analytics, data management, streaming data and AI are increasingly intermingled, all in the cloud.

Dublin, April 24, 2023 (GLOBE NEWSWIRE) -- The "Global Graph Analytics Market: Analysis By Component, By Deployment, By Enterprise Size, By Application,...

Orb is an open-source library developed by Memgraph you can use to visualize graphs by adding just a few lines to your frontend code. This blog post will show you all the cool features Orb offers and how to implement them in your project. Or don't and have slow and appalling graph visualizations - it's your choice. Seriously, use it... it's very easy and fun!

Siren promises the benefits of knowledge graphs, without the hassle of reinventing your existing data management and infrastructure

State of the art in analytics and AI can help address some of the most pressing issues in scientific research. Here is how top scientists are using them to facilitate coronavirus research.

A sui generis, multi-model open source database, designed from the ground up to be distributed. ArangoDB keeps up with the times and uses graph, and machine learning, as the entry points for its offering.

New players, new features, platforms, and ticking boxes. Let's talk graph with Neo4j.

What are the options for querying graphs, and how do we go from that to the equivalent of a virtual machine for databases?

If the history of relational databases is any indication, what is going on in graph databases right now may be history in the making.

Three-point shooting, Steph Curry, and coming up with stories. If you feel like doing your own analysis to investigate hypotheses or discover insights at any level, RDF graph's got your back. Case in point: The NBA.

Graph databases are crossing the chasm to mainstream use cases, adding features such as machine learning to their arsenal and becoming more cloud and developer friendly. Last year was a breakout year, and graph database growth and evolution is well under way in 2019.

Querying blockchains is hard, but it does not have to be. This is the vision driving The Graph.

A new open-source library by Nvidia could be the secret ingredient to advancing analytics and making graph databases faster. The key: parallel processing on Nvidia GPUs.

Here's why and how a startup founded by a duo of researchers some months back is attracting big enterprise clients and serious funding

Google uses machine learning and graphs to deliver search results. Most search engines do not. Weaviate wants to change that.

A $325 million Series F funding round, bringing Neo4j's valuation to over $2 billion. A social network of 3 billion people, distributed across 1000 servers. The latter is a demo; the former is not. But both are real signs that the graph market and Neo4j are getting huge.

GraphQL is a specification that came at just the right time to address an age-old issue in software engineering: service integration. Apollo's implementation is seeing lots of traction, and it just got more gas in the tank for its grand vision that goes well beyond integration

Knowledge graphs are probably the best technology we have for data integration. But what about application integration? Knowledge graphs can help there, too, argues EnterpriseWeb.

GDS 2.0 and AuraDS from Neo4j bring graph data science one step closer to mainstream adoption.

What do you get when you combine two of the most up-and-coming paradigms in data processing -- streaming and graphs? Likely a potential game-changer, which DARPA and others are pivoting to invest in.

What does ESG mean in the balance sheet and beyond? How can advanced analytics help organizations evaluate their impact on sustainability?

In the new knowledge-based digital world, encoding and making use of business and operational knowledge is the key to making progress and staying competitive. Here's a shortlist of technologies and processes that can support this transition, and what they are about.

Graphs are everywhere. That has been the motto of graph aficionados for years, and now the world seems to be waking up to this.

Amazon Neptune just added another query language, openCypher, to its arsenal. That may not sound like a big deal in and of itself, but coupled with updates in machine learning and data science features, it points towards the future of graph databases.

The advent of social networks, big data and e-commerce has re-emphasized the importance of analyzing a unique type of data structure- one…