Graph technology has become a requirement for the modern enterprise. Companies in virtually every industry, from healthcare to energy to financial services, are applying the power of graph analytics ...
As we've been keeping track of the graph scene for a while now, a couple of things have started becoming apparent. One, graph is here to stay. Two, there's still some way to go to make the benefits of ...
How would you feel if you saw demand for your favorite topic — which also happens to be your line of business — grow 1,000% in just two years’ time? Vindicated, overjoyed, and a bit overstretched in ...
Neo4j Inc. today announced a new serverless offering that dramatically simplifies the deployment of its graph database offering, making it easier to use with artificial intelligence applications. Most ...
REDWOOD CITY, Calif., March 16, 2020 – TigerGraph, a scalable graph database for the enterprise, unveiled TigerGraph 3.0, which delivers the power of scalable graph database and analytics to everyone ...
As the sources, types, and amounts of data continue to expand, so will the need for different kinds of analytics to make something of that data. Unfortunately, there is not a one-size-fits-all ...
These past few months have not been kind to any of us. The ripples caused by the COVID-19 crisis are felt far and wide, and the world's economies have taken a staggering blow. As with most things in ...
SAN FRANCISCO--(BUSINESS WIRE)--PuppyGraph, the first and only graph query engine, announced today its $5 million seed funding round led by defy.vc. The zero-ETL unlocks real-time graph analytics for ...
As data ecosystems become more complex, organizations are looking for advanced tools and technologies to manage and derive value from diverse and interconnected data sources. Knowledge graphs provide ...
Graph databases offer a more efficient way to model relationships and networks than relational (SQL) databases or other kinds of NoSQL databases (document, wide column, and so on). Lately many ...
Probabilistic graphs and uncertain data analysis represent a rapidly evolving research domain that seeks to reconcile the inherent imprecision of real-world data with robust computational models. By ...
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