Graphs are a general-purpose #datamodel, as relational was a general-purpose #data model a generation ago. A supply chain is a graph. Knowledge is a graph. Graphs are very applicable in a wide range of use cases @jimwebber @TheRegister #GraphDB #tech [LINK]https://www.theregister.co.uk/2020/02/05/graph_database_neo4j_chief_scientist/ [LINK]https://regmedia.co.uk/2016/04/26/graph_database.jpg

Representing Graph #Data Structures: Edge Lists, Adjacency Matrices, Adjacency Lists #datascience #analytics #tutorial #datamodel #softwareengineering #data #tech

#Neo4j #GraphDB has added sharding @adamcowley shows when and how to use it #softwareengineering #data #tech #opensource #tutorial [LINK]https://adamcowley.co.uk/neo4j/sharding-neo4j-4.0/ [LINK]https://neo4j.com/docs/operations-manual/4.0-preview/images/fabric-single-instance.png

.@CamSemantics announces Anzo #GraphDB v.2: RDF*, Custom SDK, Free Edition. "Imagine being able to do labeled properties, just like you do in Neo4j & other property graphs, but also have capability of RDF to help w ontologies & inferencing" #data #tech

Creating Graphs in Python using Networkx #dataviz #tutorial #datascience #data #tech

#Oracle offers #GraphDatabase, Graph #Analytics free of charge with purchase of main Oracle product license. 2 different products: Database built over Oracle's relational store using RDF/SPARQL. Analytics in-memory engine, custom query language

.@TigerGraph #graphDB updates its #Cloud offering with configuration for distributed graphs, replica instances for high availability, EFS for backup/restore. Updates available by end of 2019 on #AWS, #Azure to follow in Q1 2020

Graphs are common #data structures to represent #connecteddata. To use graphs with #deeplearning, we use graph embeddings, a low dimension representations which helps generalize input data. Node2Vec aims to preserve network neighborhoods #datascience #AI

Managing multiple truths in #datamodeling, #ontologies & #knowledgegraphs by @palexop #semantics #vagueness #datascience #dataengineering #EmergingTech #dmzone #presentation

Graphs #Analytics for Fraud Detection, using Graph #NeuralNetworks for Anomaly detection. GraphSAGE is Stanford #opensource project: deep neural network-based NRL toolkit, implemented in Tensorflow, making it ideal to develop an anomaly detection system

Property graphs provide a lot of flexibility in data modeling; but how do you know when to use which feature?

MP voting records are public record in the UK, detailed #data is available about how each MP has voted in each bill @PublicWhip collect data & make it accessible @joshua_e_k explores how to use a #GraphDatabase to look at MP’s voting record similarity

.@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

Graph databases are shaped the way we think, so why can't people get their heads around them?

GPU->Rapids->Cugraph->Scalable Graph Analytics

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.

Your one-stop introduction to the world of graph tech.

Read latest news about Reltio Connected Customer 360. Connected customer data at the heart of customer experience.

How can companies step themselves into the world of graph databases? Neo4j thinks it has an answer. It has been offering a Startup Program for startups with 19 employees or fewer; more than 650 startups with fewer than 20 employees took advantage of having free access to Neo4j Enterprise clusters.

.@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

CSV2RDF 2.0 released! https://t.co/2lyYzOJ6fQCSV2RDF is a streaming, transforming, #SPARQL-based #CSV to #RDF converter -now with named arguments (shout-out to @picocli) and a Docker image: https://t.co/BU9uoGXmIc— AtomGraph (@atomgraphhq) July 2, 2019

When building graph data models we frequently have to deal with a degree of polymorphism for our entities just like the real world. For instance – I’m a person, but I’m also a parent, a spouse, a sibling, a child, a… Implicit categorisation Sometimes the entity categories are entirely defined by relationships to other entities. […]

Read the first installment of this blog series on artificial intelligence on the ways graph technology adds necessary context for powerful AI solutions.

Big data, semantic searches, and real-time responses are the reason behind the growing demand for graph databases. This article talks about what a graph database is, why graph databases are popular, and why and when we should use a graph database.

A comprehensive collection of recent papers on graph deep learning - DeepGraphLearning/LiteratureDL4Graph

Stardog 6.2 just shipped with scalable virtual graph caching, better Kubernetes integration, support for Amazon Redshift, and many new optimizations . Read on for the details.

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

WHAT THIS TALK IS ABOUT My personal take on the history of human label collection ter to you

For a domain with a strong focus on unambiguous identifiers and meaning, the Semantic Web research field itself has a surprisingly ill-defined sense of identity. Started at the end of the 1990s at the intersection of databases, logic, and Web, and influenced along the way by all major tech hypes such as Big Data and machine learning, our research community needs to look in the mirror to understand who we really are…