Posts Tagged ‘graph databases’

Data Innovation, Driven By Semantic Models, Machine Learning And More

VRThe Ventana Research summit took place this week, and semantic and related technologies had a place at the table.

Among the keynoters discussing the topic of Inspiring Business Technology Innovation to Change Business and IT Forever, for example, was Nedshad Bardoliwalla, co-founder and vp of products at data prep vendor Paxata. He discussed the need to rethink how to innovate with data, as that will “drive the biggest increases in value for your organization for the foreseeable future.”

As part of that, he explained that in a world where everything physical on the planet will have a digital representation, businesses should pay attention to factors including the “massive and interesting algorithms around recognition systems, around deep learning, around semantic models that let us understand images and text in ways we never could….Take advantage of those if you are to innovate and bring capabilities to market that change way people think of data.”

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Why Graph Theory is Key to Understanding Big Data

Google

Emil Eifrem of Neo4j recently wrote, “Dr. Roy Marsten wrote in in March that Graph Theory was a key approach in understanding and leveraging big data. As a advocate of graph theory and as a developer building graph databases since 2003, it was wonderful to read someone else with similar insights and appetites. As Dr. Marsten notes, Google started the graph analysis trend in the modern era using links between documents on the Web to understand their semantic context. As a result, Google produced a Web search engine that massively outperformed its established competitors and saw it jump so far ahead that ‘to Google’ became a verb. Of course we know very well Google’s history since then: its graph-centric approach has seen it deliver innovation at scale and dominate not only in its core search market, but also across the information management space.” Read more

Find a Valentine Online Thanks to Graph Databases

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Dan Woods of Forbes recently wrote, “When it comes to dating, everybody is highly motivated. So it is no surprise that the nerdy among us put their advanced knowledge to work when seeking out a mate. The most recent celebrated example is Chris McKinlay, who used a statistical modeling approach to find which type of women to go after. The result: after 88 dates, McKinlay found the right woman for him, who, as it turns out, had been hacking her profile in a different way (see “How a Math Genius Hacked OkCupid to Find True Love”). But interest in applying technology to find love is also highlighting a shift toward graph database technology that is starting to transform applications in a large number of industries.” Read more

The Battle for Dominance in Web 3.0

Facebook

Charles Silver of Wired recently wrote, “A new battle among the tech titans has begun. What are Google, Facebook, Microsoft, Oracle, IBM and a handful of others fighting over, using vast amounts of money, hardware and top talent as weapons? This battle is over. Who will solve the scalability and performance issues of semantic computing, the data model for Web 3.0 — its arrival has been predicted annually for years but, finally, it’s on the verge. Put another way, which titan will pull off this victory feat: transforming the all-knowing ‘Star Trek’ computer—which could find the answer to any question in the universe at warp speed — from television fantasy to everyday reality.” Read more

Thinking Differently with Graph Databases

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

Bringing Together the Cloud & Semantic Tech

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

Fast Access to Complex Data through Graph Databases

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

Graph Database Adoption on the Rise

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