Graph #Analytics Market Worth $2,522 Million by 2024. Major factors fueling market growth: demand to analyze low-latency queries, integration of #AI #IoT #technologies, ability to uncover relationships in #data in #realtime #business #data #research

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

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. […]

#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

Graph databases and document databases make up a subcategory of non-relational databases or NoSQL. NoSQL databases were created to get a handle on large amounts of messy Big Data, moving very quickly. Managers use the non-relational toolkit to gain business insights and detect patterns in information on the fly, as Big Data streams into the system. Many companies, especially those with a large web presence like Google, Facebook, and Twitter, consider NoSQL databases a must-have.

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

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.

Neo4j’s Alicia Frame explains how life science researchers can exploit graph databases to get truly granular insight into big data to make major leaps forward in medical research.Complex data sets hold the key to advancing medical breakthroughs. These data sets tend to be voluminous and heterogeneous by nature, presenting an insurmountable challenge for traditional data analysis methods as they struggle to link patterns and outcomes. The unfortunate consequence is a slowdown in the progress of research.Anyone who works in life sciences is aware that they are working with highly connected information; the challenge is making sense of these connections. Unfortunately, many scientists are still using relational databases and spreadsheets which makes mapping important patterns and connections unintuitive and difficult, if not impossible.Graph technologyGraph technology is emerging as an enabler for researchers to trawl gargantuan amounts of unstructured data, turning it into valuab

In this short technical blog, I will show you how to use GSQL to search a graph for all the occurrences of a small graph pattern. We call this pattern matching. Consider the problem of matching a pattern of vertices and directed edges in a...

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Moving data between semantic and property graphs

When developing web applications with frameworks like Vue.js the best approach is to subdivide it into well-defined and reusable components for the user...

Graphileon joins CovidGraph, an initiative of graph enthusiasts and companies with the goal to build a knowledge graph with relevant information about the COVID-19 virus.

Ontotext releases GraphDB 9.2 featuring the anticipated support for RDF*/SPARQL* and improvements in the plug-ins for semantic similarity and versioning.

the task should accurately quantify the “distribution shift” in the data. Having precise control of this shift could allow us to understand the drawbacks of our learning methods, and build systems which can generalize over multiple tasks but still remember the old ones. Data distribution

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

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Relevance search is to find top-ranked entities in a #knowledgegraph that are relevant to a query entity. #Research paper on novel generative model over #knowledgegraphs for relevance search, named GREASE

Given a set of business entities such as two merchants for financial services or two doctors for healthcare, how are they related? Do they have customers or patients in common and how is their relationship evolving over a period of time?

For the interested -- you can find an empirical study of the sparql-gremlin mapping used by the plugin here: https://t.co/QGIzufzDKo— Harsh Thakkar (@Harsh9t) January 11, 2019

Where we are and where are we headed #W3CGraphWorkshop pic.twitter.com/gdKPx9e77l— Harsh Thakkar (@Harsh9t) March 6, 2019

world relationships to be recorded and analysed without losing information. Questions can then be asked of the data, such as the strength and direction of relationships between objects in the graph. Graphs are mathematical structures utilised to model numerous forms of relationships and processes in information

A look at why graphs improve predictions and how to create a workflow to use them with existing machine learning tasks.

The Journalists of the ICIJ used graph technology to understand the relationships between the leaked pieces of information in the Panama and Paradise Papers. N…

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