Bernard Marr has a good post in SmartDataCollective on the four key layers of the big data system.
Here are five big data mobile apps designed to facilitate working with big data on a mobile device.
Industry analysts have predicted that there will be somewhere between 26 and 30 billion Internet of Things, or IoT, devices by the year 2020. Will all that data actually be useful? Or will the useful data only get lost in all that noise?
Banks are facing some of the biggest challenges they've ever seen. Big data is the best answer to these problems but that's not to say that it's a quick and easy fix--nor is it to say that these are the only problems that need fixing.
Among the list of more challenging data to analyze, human brain data certainly ranks high. Thunder is a library of tools built on the Apache Spark platform specifically for use in analyzing large neural data sets.
Researchers from the Perelman School of Medicine at the University of Pennsylvania, or Penn, and the Mayo Clinic are challenging experts in science, analytics and machine learning in two competitions on detecting and predicting seizure onset.
Financial services companies should be interested in smarter computing for better and faster fraud detection, according to IBM Distinguished Engineer Jeff Calusinski.
The Consular Consolidated Database, or CCD, at the U.S. State Department has crashed. Significant problems have been occurring since July 19 and, as of this writing, there's no word on when the system will be back up again.
"The creators of big data resources like to believe that they have collected all the data relevant to their domain, that all of the data is accurate, and that the data is organized in a manner that supports meaningful data searches," Jules J. Berman, former President of the Association for Pathology Informatics, says in an interview.
Without context, data tells you worse than nothing--it derails your efforts entirely. Such is the case in using consumer payment card and loyalty card data by healthcare providers.
There are hundreds of regressions but here are ten most worthy of review by data scientists and other analytic practitioners who are working with modern data. There are also specific ways to tell which of those will work best for what you're trying to accomplish.
eBay and Redbox shared their uses and plans for predictive analytics at the Predictive Analytics World Conference in Chicago recently.
Companies are typically buying big data tools before they know what they want to do with them. That's like buying the best fishing pole on the market only to discover that now you have to build a house with it.
Mini-Sentinel is a government project that proactively seeks evidence of adverse events that can be linked to drugs on the market in an effort to find and stop problems as fast as possible. It's a big data project on a specific mission and it's hunting through huge databases of medical records--and yours is probably in there somewhere.
A grand jury in the Western District of Pennsylvania indicted five members of the Chinese military on charges of hacking and economic espionage.
An infographic built from a recent Lavastorm Analytics' survey will give you a good glimpse of what is going on in the analytics community.
While big data was introduced to the general public on the heels of the Snowden revelations with alarm and warning sirens, big data projects in the private sector have become so commonplace that they no longer see much fanfare. One of the quietest fronts is in human resources.
On this, the 100th anniversary of Turing's birth, researchers put a test before human judges to see if they could tell whether they were conversing with another human or a machine.
The international trade group is celebrating the success of its top legislative priority: President Obama's signature on the Digital Accountability and Transparency Act, or DATA Act in May.
Proving once again that simply owning the tech is not enough to hone a competitive edge, new research found that the top 100 online retailers still lag behind lesser ranked eTailers in data quality, despite owning more marketing technology.