We just launched official #JanusGraph @Docker images to simplify production deployments and testing! Check out the images at https://t.co/Vo44Bokwpu and see the docs for more info: https://t.co/VLaLwR6jcI— JanusGraph (@JanusGraph) May 8, 2019

This is what the resultant 100 retail outlet isochrone map looks like, built using Spatia and @rapidsai #cuGraph SSSP using @OrdnanceSurvey Open Roads as road graph + drive times estimated from @transportgovuk road stats #opendata cc @puntofisso pic.twitter.com/rGhDinkaVX— John Murray (@MurrayData) May 28, 2019

.@jQAssistant is a #QA tool which allows definition & validation of project specific rules on a structural level. Built upon #Neo4j #graphdatabase, can be plugged into build process. Now w/ #PlantUML class diagrams #dataviz #softwareengineering

Just published a new version of neovis.js, a JavaScript graph visualization package designed to be used with @neo4j Graph Algorithms🎉This version includes support for TypeScript and a bugfix impacting some Angular issues. Kudos to Shoval for the PRs!https://t.co/1lXCZhgx1r pic.twitter.com/IsdLZxlhKX— William Lyon (@lyonwj) October 4, 2019

Visualize Kafka Streams with Neo4j by taking any data, turning it into a graph, leveraging graph processing, and piping the results back to Apache Kafka, adding visualizations to your event streaming applications.

Discover how graphs are everywhere with the second installment of this series from Joe Depeau as he takes to graphing the complex world of tea.

.@DDIAlliance releases XKOS v1.2 (Extended Knowledge Organization System) specification →facilitates sharing & management of statistical classifications.(For building #Semantic Taxonomies to tag Datasets & #DataScience outputs)https://t.co/HZD1nl5Nu0#BigData #Ontologies #RDF pic.twitter.com/DDYbHqCvm7— Kirk Borne (@KirkDBorne) June 21, 2019

Image taken from wikipedia, Couple of days ago I was presenting “How to use similarity algorithms” in a Neo4j online meetup with Mark Needham. Among other use-cases we discussed how the…

There are only a handful of publicly available knowledge graphs. And among those, only a few provide data with enough breadth to in some way represent the entire internet, and with enough granulari…

n

#KnowledgeGraphs and their central role in #bigdata processing: Past, Present, and Future #keynote #presentation #research #AI #datascience #data @amit_p

19, trying to make sense of the data surrounding the virus is a Herculean task. Vast in volume and ceaselessly produced, this data emanates from domains as different as virology and economics and is produced by a multitude of people and organizations. Unsurprisingly the standards to which this data conforms are as multitudinous as its sources. It just so happens that making sense of messy data from disparate sources is one of the things at which knowledge graphs excel. Moreover, knowledge graphs make it possible to derive new knowledge from intelligently connecting information residing in those disparate data repositories. Given that the ability to better analyze data and gain new insights is of obvious use to people trying to respond to the pandemic, those working with knowledge graph technologies have started to talk about how those technologies – and their skills – might

At Stardog, we’re all about making it easy to unify data. That’s why we’ve just open sourced a set of tools to make working with Stardog even easier than before.

Dependencies, like graphs, are everywhere. Achieving a goal is rarely possible in a vacuum and requires collaboration between individuals and/or process...

This is a summary of a short talk I gave internally at the ODI to help illustrate some of the important aspects of data standards for non-technical folk. I thought I’d write it up here too, i…

GQL is an effort to standardize property #GraphDB query language. GQL project lead leaving #Neo4j, WG3 is now responsible. "There is no certainty about the success of the GQL project, but there are good objective and subjective grounds for optimism"

#smartcity stakeholders must build #datagovernance & management ready for all categories of #data. Required:assessment of expected current/future categories, scalable, flexible, modular design. Shared, unified, standards-based graph data model: #ontology

Graph databases are finding a place in analytics applications at organizations that need to be able to map and understand the connections in large and ...

Graphs are an excellent way of encoding domain knowledge for your business data. One of the popular databases for graphs is Neo4J and I…

ArangoDB Oasis is the managed service for ArangoDB. It provides fully hosted, managed and monitored cluster deployments of any size.

An analysis of the unique problem @TAKEALOT faced in facilitating reliable deliveries to customers and how they use a #graphdatabase to deliver a performant and scalable solution, by @filipe_ppt #retail #business #innovation #data https://dev.to/fppt/managing-delivery-networks-a-use-case-for-graph-databases-2jb0

London, United Kingdom — December 01, 2018 — Memgraph, creator of the real-time distributed enterprise graph database platform, has…

On the day after Cinco de Mayo, it's Christmas for Azure database pros. Cosmos DB, Azure Database and Azure SQL Data Warehouse all see significant updates.

It was hard to top last year’s demonstration of the new Microsoft 365 experienced powered by the Microsoft Graph. The focus this year shifted to how the graph could break down application and device silos to make everyday applications more user-centric.

at Build 2017. Azure Cosmos DB was designed t be a superset of Microsoft's existing NoSQL Document DB database. Its codename was "Project Florence," and Microsoft execs consider it a "born in the cloud/cloud native" database that's designed to be scalable and usable by customers of any size.Microsoft currently charges by provisioned throughput and consumed storage by the hour for Azure Cosmos DB. Before the introduction of the free tier, M

When we used to think about data, it was most often in regard to where data was going to be stored and how we would manage it. Yes, files worked for a while, but when manipulating data became an important business priority across industries, the “file” solution didn’t work so well anymore. To meet these increasing demands, applications were designed and developed, addressing data storage and manipulation needs simultaneously — thus, the “database” was born. Today, data is viewed quite differently. Beyond data manipulation, organizations are focusing on mining their data for more visibility into and a deeper understanding of the intelligence within that data. Utilizing the insights acquired from their data to help make informed business decisions is a key priority for business leaders and a major concern in the development, evolution, and adoption of database solutions. A new term emerged in the industry — digital assets. That data the world has been obsessing over.

Using libgraph for graph data structures

Discover what's new in the 1.1 release of the Neo4j Bloom graph data visualization tool, including internet browser capabilities and new role-based security.