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Big Data in Security – Part III: Graph Analytics

TRACFollowing part two of our Big Data in Security series on University of California, Berkeley’s AMPLab stack, I caught up with talented data scientists Michael Howe and Preetham Raghunanda to discuss their exciting graph analytics work.

Where did graph databases originate and what problems are they trying to solve?

Michael: Disparate data types have a lot of connections between them and not just the types of connections that have been well represented in relational databases. The actual graph database technology is fairly nascent, really becoming prominent in the last decade. It’s been driven by the cheaper costs of storage and computational capacity and especially the rise of Big Data.

There have been a number of players driving development in this market, specifically research communities and businesses like Google, Facebook, and Twitter. These organizations are looking at large volumes of data with lots of inter-related attributes from multiple sources. They need to be able to view their data in a much cleaner fashion so that the people analyzing it don’t need to have in-depth knowledge of the storage technology or every particular aspect of the data. There are a number of open source and proprietary graph database solutions to address these growing needs and the field continues to grow.

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A Room with a View (of Crucial Big Data Insights)

What’s the problem with Big Data? You guessed right — it’s BIG.

Big Data empowers organizations to discern patterns that were once invisible, leading to breakthrough ideas and transformed business performance. But there is simply so much of it, and from such myriad sources — customers, competitors, mobile, social, web, transactional, operational, internal, external, structured, and unstructured — that, for many organizations, Big Data is overwhelming. The torrents of data will only increase as the Internet of Everything spreads its ever-expanding wave of connectivity, from 10 billion connected things today to 50 billion in 2020.

So, how can organizations learn to use all of that data?

The key lies not in simply having access to enormous data streams. Information must be filtered for crucial, actionable insights, and presented to the right people in a visualized, comprehensible form. Only then will Big Data transform business strategies and decisions. In effect, Big Data must be made small.

However, as McKinsey & Co. reported, many organizations don’t have enough data scientists, much less ones who understand the business well enough to draw conclusions. The trick is to get the scientists together with the experts who understand the business levers driving the organization. Put them in a room with the right tools, and watch the synergy fly.

But what sort of a room?


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Unlocking Wi-Fi Enabled Value-Added Services

Competing with the virtual, e-commerce world is becoming increasingly challenging for real-world businesses. Traditional retailers have long envied the massive amounts of valuable data that online retailers have available to help them better understand customer behavior and implement winning marketing tactics. Online retailers know valuable information such as how frequently customers return, how long they spend on their sites, what the customers looked at but didn’t buy, and where they went before and after coming to the site. Businesses as diverse as hotels, banks, stadiums, airports, and large public venues are all looking for ways to get similar detailed data on customer activities in their facilities, so they can improve the customer experience and their bottom lines. The data and insights have not been available to bricks-and-mortar facilities, until now.

That situation is changing through the growing availability of Wi-Fi in business locations. Many retailers, hotels, and other businesses are increasingly offering Wi-Fi as a service that allows their customers to connect mobile devices to the Internet. Hidden in this valuable service is a vast amount of information and insight, which retailers and others can use to deliver tangible value to their bottom lines. Hypersensitive location information, device details, identification of returning customers, and sophisticated path analysis are just some of the customer data captured by Wi-Fi networks. Businesses are now realizing that the data and capabilities offer new ways to improve the customer experience and support a range of market-leading monetization models.

For many businesses, these new location-based experiences and Read More »

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Big Data in Security – Part II: The AMPLab Stack


Following part one of our Big Data in Security series on TRAC tools, I caught up with talented data scientist Mahdi Namazifar to discuss TRAC’s work with the Berkeley AMPLab Big Data stack.

Researchers at University of California, Berkeley AMPLab built this open source Berkeley Data Analytics Stack (BDAS), starting at the bottom what is Mesos?

AMPLab is looking at the big data problem from a slightly different perspective, a novel perspective that includes a number of different components. When you look at the stack at the lowest level, you see Mesos, which is a resource management tool for cluster computing. Suppose you have a cluster that you are using for running Hadoop Map Reduce jobs, MPI jobs, and multi-threaded jobs. Mesos manages the available computing resources and assigns them to different kinds of jobs running on the cluster in an efficient way. In a traditional Hadoop cluster, only one Map-Reduce job is running at any given time and that job blocks all the cluster resources.  Mesos on the other hand, sits on top of a cluster and manages the resources for all the different types of computation that might be running on the cluster. Mesos is similar to Apache YARN, which is another cluster resource management tool. TRAC doesn’t currently use Mesos.


AMPLab Stack

The AMPLab Statck

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When Your Collaboration Infrastructure Starts Making Odd Noises

If your car is overdue for a tune-up, it may let you know in unexpected (and unsettling) ways — rough handling, sluggish acceleration, and even an odd (“that can’t be good”) noise from under the hood. If you’re like me, you don’t want to find yourself waiting on the side of the road for a tow truck. You schedule your car for regular tune-ups to make sure your tires aren’t worn, the wheels are aligned, no fluids are leaking, and the engine is performing to the right specifications.

Just like your car, a collaboration infrastructure needs regular tune-ups. In fact, just like your car, a collaboration infrastructure will let you know that it’s not running optimally. But by the time you actually notice the performance problems with collaboration applications, the odds are that those problems have already started causing issues with your end-users.

Traditionally, optimization has been looked at (even by Cisco in the early days) as the final step in the deployment cycle. But IT projects queue up so fast that optimization for the last project may not happen because the next project is already underway. Today, however, we look at optimization in an Read More »

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