Page 15 - GCN, Oct/Nov 2016
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The field of advanced analytics continues to evolve, as organizations seek new ways to derive intelligence from data—or to derive intelligence from a greater spectrum of data formats, including text, video and social media.
THIS SOFTWARE IS ALL EYES
SPONSORED CONTENT
Identifying andT
analyzing visual images is critical for security applications.
HE GROWTH of social media and the widespread use of mobile devices have led to an explosion of photos and videos posted on the Internet. These images are an increasingly
puts some very interesting image-understanding capabilities in the hands of regular users. The software is capable of analyzing and searching pictures, video, and satellite images, based on their visual content, much like an actual person would. It can separate man- made items from naturally occurring things; understand context in an image; distinguish faces and facial features; identify; recognize and automatically label objects; detect and recognize text in several languages; and create descriptions on the fly for everything it sees.
Computer vision, machine learning, artificial intelligence, are all very difficult things. We have worked hard to make these technologies work for the user in a transparent way so that they can interact with the system intuitively.
Pictures and video have long been effective weapons in the fight for national security and are invaluable to our intelligence and defense communities. With new advances in AI-based software,
we are making powerful analysis capabilities more broadly available.
Read more from Joe Santucci at carahsoft.com/innovation/Santucci.
JOSEPH SANTUCCI PRESIDENT & CEO PIXLOGIC INC.
important part of research, security, and investigations at all levels of government. But the sheer volume makes it nearly impossible for those assigned to review and tag these images to make analysis possible.
Being able to locate and analyze all types of images based on their visual content is something the artificial intelligence (AI) community has been working on for the past 40
years. It requires teaching a machine how to see and understand by encapsulating the way a person sees and comprehends using both their eyes and brain.
Working with some demanding customers, including the US government, my company has been able to create software that
IMPROVING DATA-DRIVEN DECISION-MAKING
Employing daT
ta wrangling techniques can help expedite advanced data analytics.
O BEAT the clock on time-sensitive big data initiatives—security threat detection, disease suppression, emergency evacuation planning— organizations are focusing on how to
Data wrangling has proven its worth to a multitude of organiza- tions with varying use-cases from cybersecurity and fraud detection to natural disaster mitigation to predictive maintenance and repair. When it comes to detecting fraud, for example, agencies are using advanced data wrangling to combine and standardize a diverse set
of user, system, and application data that allow them to more quickly identify suspicious behavior. To address evacuation plans for natural disasters, agencies are leveraging real-time and historical weather data with geospatial data and third-party travel systems data to better predict the impact of an impending disaster.
In all of these examples, providing decision-makers with access to raw data allows agencies to wrangle data faster and to more accurately identify the right patterns. While seemingly simple, the time saved from more efficient data wrangling can mean all the difference in suppressing an outbreak, ceasing a security breach, or identifying and correcting
a defective airplane part. Data wrangling has never been so readily available as it is today, empowering today’s organizations to unlock the potential of their data.
Read more from Adam Wilson at carahsoft.com/innovation/Wilson.
ADAM WILSON
CEO, TRIFACTA
make their traditional processes more efficient. The biggest culprit? Data wrangling, or the process of converting raw data into a format that is usable for analysis, which consumes up to 80
percent of any analysis process.
With the right tools, organizations can eschew traditional processes
for a more agile solution, one that automates the bulk of this process and allows the analysts to work directly with the raw data and quickly transform it into the required format for analysis. It has tremendous impact on the entire analysis process. Cutting out the (technical) middleman helps organizations scale wrangling operations across the entire organization, accomplishing more analysis in less time. Not only that, but those who aren’t ensconced in the business context of the problem naturally won’t deliver the most accurate results.
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