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

Eric Little of Bio-ITWorld recently discussed how private cloud technologies can be used to improve semantic technologies. He writes, “While semantic technologies provide a sophisticated way of modeling complex relationships between data, the graphs that are created within semantic solutions can quickly grow to enormous sizes, given that they capture not only the elements contained within an enterprise’s raw data, but the added litany of related facts and relationships generated by automated reasoning, where 10-100 times as much new data can be generated from a single data source. As an example, imagine taking one’s raw assay data on a given compound, then linking it to all known data about related clinical studies and phenotypic effects, as well as underlying genomics data.” Read more

Emil Eifrem, founder of Neo4j has written an article for Mashable about the rise of graph databases. He writes, “Until the NOSQL wave hit a few years ago, the least fun part of a project was dealing with its database. Now there are new technologies to keep the adventuresome developer busy. The catch is, most of these post-relational databases, such as MongoDB, Cassandra, and Riak, are designed to handle simple data. However, the most interesting applications deal with a complex, connected world. A new type of database significantly changes the standard direction taken by NOSQL. Graph databases, unlike their NOSQL and relational brethren, are designed for lightning-fast access to complex data found in social networks, recommendation engines and networked systems.” 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