Graph databases and RDF: It’s a family affair
RDF is a graph data model you've probably either never heard of, or already dismissed. Why is that, could there be value in it, and how does it differ from the most popular graph data model out there?
Read More →The continuing rise of graph databases
Graph technology is well on its way from a fringe domain to going mainstream. We take a look at the state of the union in graph, featuring Neo4j's latest release and insights as well as data and opinions from Cloudera, DataStax, and IBM.
Read More →Advertising data going forward: Transparency and scale
The problem with advertising data and what to do about it. Plus, the future of big data architecture, and other stories from the Ad Tech trenches
Read More →Caffe2: Deep learning with flexibility and scalability
As the AI landscape continues to evolve, a new version of the popular Caffe open source deep learning framework has been released. Caffe2 is backed by Facebook and features a wide array of partnerships to make it as flexible and scalable as possible. But is that enough to make Caffe2 a winner?
Read More →DataOps: Changing the world one organization at a time
Uncertain about the benefits of going data-driven? Not sure how to get there? Here's some advice from the people who built Facebook's data infrastructure
Read More →Planet analytics: big data, sustainability, and environmental impact
What is the relation between big data applications and sustainability? What is the net effect of improved efficiency versus increased resource consumption, who gets to measure this, and how?
Read More →Language agnostic document processing: Finding relations using statistics, machine learning, and graphs
Would you like to be able to find related work regardless of domain or language, more efficiently than you ever thought possible? Omnity is out to help achieve this, using a mix of techniques.
Read More →Data Works, Hadoop 3.0 is round the corner, and ‘Horton Hatches the Egg’
What's coming in Hadoop 3.0? How is Hortonworks' strategy evolving? And what are some examples of open-source powered innovation in big data use cases?
Read More →LinkedIn: Machine learning is like oxygen, but the human element is not going away anytime soon
How do data and machine-learning powered algorithms work to control newsfeeds and spread stories? How much of that is automated, how much should you be able to understand and control, and where is it all headed? LinkedIn has answers.
Read More →Please wait while your future trickles down: innovation, asymmetry and evolution
If you believe the technologists, the future is around the corner and it’s going to be perfect. But is more technology really the answer to every problem? The future’s so bright, i gotta wear shades Do you know what CeBIT is? Chances are you do, because here you are reading this. This means you have […]
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