Monthly Archives: November 2013

Object Storage: The Next Storage Paradigm

Object Storage: The Next Storage Paradigm

Jim O’Reilly Object storage is evolving from a data archive to the primary form of storage in large systems. I remember my first object store in 2007. Using a COTS x 86 server with 6 TB of storage, it was powered by Caringo software . I needed a cluster […]

Five ways to handle Big Data in R

Five ways to handle Big Data in R

Five strategies to tackle big data with R Big data was one of the biggest topics on this year’s useR conference in Albacete and it is definitely one of today’s hottest buzzwords. But what defines “Big Data”? And on the practical side: How can big data be tackled in […]

Hedge Funds Pick Nuggets From Online Social Conversations

Hedge Funds Pick Nuggets From Online Social Conversations

Kishore Jethanandani Hedge funds don’t dismiss the gaggle on social media sites as mere white noise but another source of data to gain alpha. Machine learning and natural language processing algorithms aid in sifting through the chaff of social media data and zero down on the valuable gems. “Social […]

Alpine Data Analytics App Works Directly Against Hadoop

Alpine Data Analytics App Works Directly Against Hadoop

Slide Show Big Data: Not Just for Big Business Anymore The popular perception is that anything involving petabytes of data requires a lot of IT people and at least one data scientist to analyze. In reality, however, analytics applications are scaling to the point where analysts can now analyze […]

UPS Nets Huge Fuel Savings With Analytics

UPS Nets Huge Fuel Savings With Analytics

5 Big Wishes For Big Data Deployments (click image for larger view and for slideshow) Constructive dissatisfaction. That’s what UPS calls its ongoing quest for process improvement that brought about ORION, an On-Road Integrated Optimization and Navigation system that will save the shipper 1.5 million gallons of fuel in […]

Handling Big Data Backup & Recovery

Handling Big Data Backup & Recovery

John Edwards The fact that big data systems and applications must be supported by a fast and powerful recovery strategy is undeniable. There is clearly a growing need for more efficient ways to move massive data volumes over a WAN and to manage backup restoration from a holistic, enterprise-wide […]

Spatial Clustering With Equal Sizes

Spatial Clustering With Equal Sizes

Cluster Map This is a problem I have encountered many times where the goal is to take a sample of spatial locations and apply constraints to the algorithm.  In addition to providing a pre-determined number of K clusters a fixed size of elements needs to be held constant within […]