GraphNews

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

Knowledge graphs are among the most important technologies for the 2020s. Here is how they are evolving, with vendors and standard bodies listening, and platforms becoming fluent in many query languages

In its new release, Neo4j addresses key concerns for enterprise adoption. Scalability, security, management and architectural changes are here. And so is a strange feeling of deja-vu, too.

Actions and words, code and advocacy. DataStax is changing strategy, re-engaging with the Apache Cassandra open source community, and releasing some interesting technical advancements while at it, too.

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.

A hitherto under the radar graph database that uses blockchain to support data lineage and verification wants to take over the world, starting with the US Department of Defense

GraphQL was never conceived as a query language for databases. Yet, it's increasingly being used for this purpose. Here's why, and how.

Session presented at Big Data Spain 2018 Conference14th Nov 2018Kinépolis Madrid

Data lakes can be a great asset, but they need an array of elements to work properly. We take a look at how it works for Montefiore Health System and discuss the role of semantics and graph databases in the data lake architecture.

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?

Explainable AI in real life could mean Einstein not just answering your questions, but also providing justification. Advancing the state of the art in natural language processing is done on the intersection of graphs and machine learning.

A tour de force on progress in AI, by some of the world's leading experts and venture capitalists.

Research and industry breakthroughs, ethics, and predictions. This is what AI looks like today, and what it's likely to look like tomorrow.

Gary Marcus, a prominent figure in AI, is on a mission to instill a breath of fresh air to a discipline he sees as in danger of stagnating. Knowledge graphs, the 20-year old hype, may have something to offer there.

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.