GraphNews

New preprint by Ana Claudia Sima & Tarcisio Mendes de Farias et al, with @cdessimoz and many who aren't on Twitter: Enabling Semantic Queries Across Federated Bioinformatics DatabasesFederating @OMABrowser @uniprot @Bgeedb #SPARQL #RDFhttps://t.co/zHHgrSMH8K— Marc RobinsonRechavi #WearADamnMask (@marc_rr) July 1, 2019

My thoughts on the future of @apachetinkerpop.I believe:Apache TinkerPopA Graph Computing Frameworks==should become==>Apache TinkerPopA Distributed Computing Virtual Machine and Languagehttps://t.co/Z5BKnewpS0 #graphdb #nosql pic.twitter.com/yX2wvoRaYQ— Marko A. Rodriguez (@twarko) February 26, 2019

Am I the only one who thinks there's nothing really wrong with the #RDF stack?To me it's such a powerful technology that 20 years in we're still exploring what it is capable of. To do that, we need to throw out the old software design paradigms and think graph- and Web-native.— Martynas Jusevicius (@namedgraph) November 27, 2018

Massively parallel implementation of #Graph2Vec = scalable graph representation #algorithm learns vectors that describe whole graphs in an embedding space:https://t.co/nR0Iv2QG4u by @benrozemberczki#BigData #DataScience #AI #MachineLearning #LinkedData #GraphDB #GraphAnalytics pic.twitter.com/7INPwfeDPw— Kirk Borne (@KirkDBorne) August 25, 2019

Researchers thought that they were five years away from solving a math riddle from the 1980's. In reality, and without knowing, they had nearly cracked the problem already. Researchers from the University of Copenhagen and the Technical University of Denmark (DTU) thought that they were five year

Meaning-First data modeling for #semantic #knowledgegraphs: a replacement to Structure-First modeling. The relational model was a great start for #datamanagement, but it is time to embrace a radical return to simplicity: Meaning-First data modeling #data

London, United Kingdom — February 05, 2018 — Today, Memgraph, creator of the real-time distributed enterprise graph database platform, has…

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

up the value of e anytime I need it (figure 1);Figure 1. A data dictionary with key and value of arithmetic expressions.(ii) if I do not have that option then the only other alternative to get the value of e is to actually compute the arithmetic expression and get the corresponding value. The first method, let’s call it the data/memorization method, which does not require me to know how to compute e while the second does. That is, in using the second method I (or the computer!) must know the procedures of addition and multiplication, shown in figure 2 below (where Succ is the ‘successor’ function that returns the next natural number).Figure 2. Theoretical definition of the procedures/functions of addition and multip

Recycle, Reuse and Reduce also for data models! Why waste time remodeling the same data, just because you go to graph? Learn how to auto-generate graph data models (for Neo4j) from legacy data models in UML, XML, ERD, concept maps and other formats. Missing something on the list? Let the author know! New: FileMaker db's as Graph Data Models!

This is so cool, this is the first time for me to ship a #Scala REPL based product. The @apachetinkerpop based DSL allows to query your own codebase for security vulnerabilitries, data leaks etc. https://t.co/1xSOwCnhI3— Michael Pollmeier (@pollmeier) December 11, 2018

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

#Microsoft Project Cortex Builds A Custom #KnowledgeGraph for Your Company. Service brings #AI to the vast quantities of #data companies store within #Microsoft365. Unlocks knowledge stored about a topic/client/project inside of documents, surfaces them

k #datascience #covid19 #predictivemodel

#NLP is now good enough that we can measure how well systems reads text in terms of extracted knowledge: Knowledge Base Population @diffbot released KnowledgeNet reproducible #dataset to measure this, w #opensource state-of-the-art baseline #research #AI

How "talking semantics" can help you perform better #DataScience. @palexop #StrataData talk in New York slides now available #dataModeling #semantics

A blueprint for the next @apachetinkerpop and an analysis on #opensource. @twarko @_mmadt #presentation from @DataDayTexas #GraphDB #data #tech #cloud #insight #softwareengineering #softwaredevelopment #AWS

The first public presentation of mm-ADT at ApacheCon 2019 in Las Vegas, Nevada.

some TypeScript type system features, such as discriminated type unions, powerful type inference, nullability checking, and type intersections, present an opportunity to both model what Schema.org-conformant JSON-LD looks like, while also providing ergonomic completions to the developer

Recently, I published schema-dts ( npm , GitHub ), an open source library that models JSON-LD Schema.org in TypeScript. A big reason I wanted to do this…

Today there are also strong user stories in the contexts of investigative work flows based on sets of suspects in graph-based analysis of crime, intelligence, fraud, churn, recommendations and other behavior / networking analytical areas.

Google applied the PageRank algorithm to rank pages to prioritize search results. The algorithm is applicable to networks in other domains, for example, interest rate swap transaction networks in banking, real estate transaction networks, and social networks to find the most influential members of t

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.

prem or both. The discovery and integration layer in the fabric integrates and blends data, drawing on subsets of data from across the underlying data landscape as required.