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AFCEA Answers: The Five "Vs" of Big Data

September 13, 2013
By Max Cacas

In considering how best to manage the challenges and opportunities presented by big data in the U.S. Defense Department, Dan Doney, chief innovation officer with the Defense Intelligence Agency (DIA), says the current best thinking on the topic centers around what he calls, “the five Vs”.

Appearing on a recent episode of the AFCEA Answers radio program, Doney says it’s important to always consider “volume, velocity, variety, veracity and value” when trying to manage and take advantage of big data.

“Volume gets the most attention,” he says, noting that most people focus on datasets measured in terabytes and petabytes. “In fact, though, that’s the one in which we’ve made the most progress. When it comes to “velocity,” or the rate at which large datasets often pour into servers, Doney notes that many algorithms originally designed for static databases now are being redesigned to handle datasets that require disparate types of data to be interconnected with metadata to be useful.

Doney goes on to say that “variety” remains one of the last three challenges when it comes to big data for his agency because of the DIA’s mandate to create a “big picture” that emerges from all that information. And he says that solutions have so far not caught up with the DIA’s needs.

Doney says “veracity,” or the “ability to put faith behind that data,” becomes a challenge when one needs to put equivalent amounts of context to disparate data types to add important detail to that “big picture.”
 

Brian Weiss, vice president, Autonomy/HP, says that when it comes to “value” in consideration of big data, some of the most exciting innovation is coming in terms of how to distinguish and sort out important information from the huge datasets.

Szykman: Turning Big Data Into Big Information

August 30, 2013
By Max Cacas

 
Current efforts to deal with big data, the massive amounts of information resulting from an ever-expanding number of networked computers, storage and sensors,  go hand-in-hand with the government’s priority to sift through these huge datasets for important data.  So says Simon Szykman, chief information officer (CIO) with the U.S. Department of Commerce.
 
He told a recent episode of the “AFCEA Answers” radio program that the current digital government strategy includes initiatives related to open government and sharing of government data. “We’re seeing that through increased use of government datasets, and in some cases, opening up APIs (application programming interfaces) for direct access to government data.  So, we’re hoping that some of the things we’re unable to do on the government side will be done by citizens, companies, and those in the private sector to help use the data in new ways, and in new types of products.”
 
At the same time, the source of all that data is itself creating big data challenges for industry and government, according to Kapil Bakshi, chief solution architect with Cisco Public Sector in Washington, D.C.
 
“We expect as many as 50 billion devices to be connected to the internet by the year 2020.  These include small sensors, control system devices, mobile telephone devices.  They will all produce some form of data that will be collected by the networks, and flow back to a big data analytics engine.”  He adds that this forthcoming “internet of things,” and the resultant datasets, will require a rethinking of how networks are configured and managed to handle all that data. 
 

Dealing with Big Data Takes an Ecosystem

May 21, 2013
By Max Cacas

Effectively dealing with data sets measured in terabytes and petabytes sometimes takes an ecosystem. And at times, that ecosystem is dependent on metadata, a sub-dataset that describes the dataset so that it can be analyzed quickly.

That’s according to Todd Myers, a big data specialist with the National Geospatial-Intelligence Agency (NGA), who spoke at the AFCEA SOLUTIONS Series - George Mason University Symposium, "Critical Issues in C4I," on Tuesday.

Myers said that in an era when an intelligence community analyst no longer has the luxury of staring at a monitor for hours poring over a video feed searching for “that one delta that will deliver a needed clue,” properly applied metadata can provide the speed needed for what he calls “contextual resolution."

One firm that seeks to help analysts sift through big datasets is sqrrl. Ely Kahn, chief executive officer of sqrrl, said his firm relies on open source big data tools like Hadoop to provide analysis with “low latency,” the big data code for speed and efficiency. He told symposium attendees that one of the most interesting aspects of both big data and open source is that they have helped create new ways to write the applications that are being used to unlock the secrets in big data.

Georgia Tech to Research Big Data Solutions

December 3, 2012
George I. Seffers

 
A research team at the Georgia Institute of Technology has received a $2.7 million award from the Defense Advanced Research Projects Agency (DARPA) to develop technology intended to help address the challenges of big data—data sets that are both massive and complex. The contract is part of DARPA’s XDATA program, a four-year research effort to develop new computational techniques and open-source software tools for processing and analyzing data, motivated by defense needs. Georgia Tech has been selected by DARPA to perform research in the area of scalable analytics and data-processing technology. The Georgia Tech team will focus on producing novel machine-learning approaches capable of analyzing very large-scale data. In addition, team members will pursue development of distributed computing methods that can process data-analytics algorithms very rapidly by simultaneously utilizing a variety of systems, including supercomputers, parallel-processing environments and networked distributed computing systems. 

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