“We want to help the world make sense of data and we think graphs are the best way of doing that.”

That’s the word from Emil Eifrem, CEO of Neo Technology, which makes the open-source Neo4j NoSQL graph database. He’s not talking in terms of RDF-centric solutions, even though he says he’s 100 percent in agreement with the vision of the semantic web and machine readability. “The world is a graph,” Eifrem says, “and RDF is a great way of connecting things. I’m all in agreement there.” The problem, in his opinion, is that execution on the software end there has been lacking.

“This comes down to usability,” he says, and the average developer, he believes, finds the semantic web-oriented tools largely incomprehensible. Eifrem says he’s speaking from real-world experiences, having worked directly with RDF and taught classes on the semantic web layers. Where it took a week to get students up to speed on things like Jena and Sesame, they ‘get’ the property graph and graph databases in half-a-day, he says. Neo4j stores data in nodes connected by directed, typed relationships with properties on both – also known as a property graph.

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Emil Eifrem, CEO of Neo Technology recently opined that graphs offer a new way of thinking. He explains, “Faced with the need to generate ever-greater insight and end-user value, some of the world’s most innovative companies — Google, Facebook, Twitter, Adobe and American Express among them — have turned to graph technologies to tackle the complexity at the heart of their data. To understand how graphs address data complexity, we need first to understand the nature of the complexity itself. In practical terms, data gets more complex as it gets bigger, more semi-structured, and more densely connected.” Read more

Emil Eifrem, CEO of Neo Technology recently wrote an article highlighting the recent rise in graph database adoption. He writes, “Graph databases are the most scalable, high performance way to query and store highly interconnected data. They help improve intelligence, predictive analytics, social network analysis, decision and process management – which all involve highly connected data with lots of relationships. A relevant use case for graph databases is the social graph. The social graph leverages information across a range of networks to understand the relationships between individuals. Facebook, LinkedIn and Amazon are all examples of companies that derived tremendous value from leveraging social and professional graphs and providing a deeper analysis of the data they collect every day. The biggest challenge that companies face is the ability to handle the exponential growth and massive connected data challenges associated with the social graph.” Read more here. Read more