People. Patterns. Predictions. Meet the new NEC Advanced Recognition Systems.

This week I am celebrating my sixth anniversary with NEC. I recall that my original trip to visit the Headquarters in Tokyo was postponed by the unfortunate events of the 2011 Tsunami and earthquake.

Since then, we have established our Center of Excellence in North America and extended our offering to U.S. Federal clients. We also introduced a number of products and services, including ground breaking cloud-based Identity as a Service (IDaas), and we solidified our position as the premier provider to Law Enforcement and public safety clients in the United States.

These days, rather than thinking about the past I’ve been spending a lot of time thinking about the future. A future where I see continued proliferation of biometrics use, increased emphasis on crime prevention and a convergence of “identity” with access management. Through advancements in data analytics and artificial intelligence (AI), our biometrics technology can evolve from technology used to determine where people have been and what they may have done, to predict where people will go and what they will do.

In response to these emerging market trends, today I’m proud to announce that we have rebranded our former biometrics solutions division to NEC Advanced Recognition Systems. I believe that biometrics coupled with high-powered analytical engines can predict and positively alter our travel experiences providing easier access, shorter lines and improved utilization of resources; recognize patterns for real-time monitoring, threat assessment and escalation and through it all provide tools for improved planning and forecasting.

Want more information about a Safety and Security solution from NEC?
Want more information about a Safety and Security solution from NEC?

To underscore our mission and align our products and services the new Advanced Recognition Systems group will give emphasis to three key words: People, Patterns and Predictions.

People. Our primary mission is to serve citizens and the people who protect them. Whether keeping the public safe at home, supporting troops overseas, improving the experience of travelers, or providing the right identity at the right time, our advanced recognition systems supply trusted intelligence to help build safer and brighter communities.

Patterns. From fingerprint pattern recognition to arrangements of accessible data, sequences of critical information are everywhere—you just have to know where to look. Our cutting-edge advanced recognition systems can pinpoint valuable patterns for solving crime, strengthening national security, and identifying trends and efficiencies for tech-savvy businesses. All to help enable diverse missions and realize the possibilities.

Predictions. Our advanced recognition systems can transform the efficiency of your team. Instead of simply gathering and reporting data, our technologies analyze intelligence to predict public safety threats, alert agencies to emerging global concerns, pinpoint potential risks in high-traffic venues, and provide invaluable input to critical business decisions.

Our new name better aligns our extensive local and global capabilities in meeting the all-encompassing needs of our clients. Using our systems integration approach to the market we are committed to understanding our clients’ challenges first, and assist them with a full solution implementation in comparison to any specific biometric technology or tool.

While finding success in reaching these new markets, we remain committed to our roots and will continue to consistently provide high-quality, accurate solutions for government and public safety markets.

I’d like to thank everyone who has participated in NEC’s success in the past six years and who have also contributed to the study and launch of this new vision. I hope you’ll take a look around the new Web site and help us spread the word.

Here’s to the future!

Advanced Recognition Systems
People. Patterns. Predictions.

Advanced Recognition Systems and Solutions

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Empower End-Users with Real-Time Analytics And Other Reasons to Upgrade to Cognos 10

Business intelligence is only powerful if it is easily accessible. By putting the end-user in the driver’s seat, businesses can maximize analytics and data without putting a burden on the IT department to run reports. Not only does this improve productivity, it puts the data in the hands of those who can do the most with it. Frankly, this level of end-user empowerment is a solid-enough reason to upgrade IBM’s Cognos software to the latest V10.2. But don’t take our word for it. Take a look at the impact first hand.

Cincinnati Zoo Saves more than $100,000 per Year

The Cincinnati Zoo was challenged with increasing attendance and revenues while also enhancing the customer experience. It also needed to boost sales on food and in the retail outlets and optimize labor costs. These are no small tasks! By implementing IBM Cognos V10, the zoo was able to utilize business analytics that allowed the zoo staff to use iPads to track the information real-time. The results were incredible.

“By being so closely aligned with the day-to-day interactions within the zoo, the team could spend more time with visitors and see even the most basic of changes that were needed, said Marti Walsh, Channel Enablement Program Manager with IBM Analytics. “For example, perhaps traffic patterns showed that moving a hot dog stand to a different location would result in heavier traffic to the stand. That could be marked immediately on the spot, rather than waiting to return and transcribe notes into a database.”

Real-time capture of the #data resulted in savings of more than $40,000 on marketing in the first year Click To Tweet, and more than $100,000 per year by identifying less-effective promotions and discounts. Plus, the zoo increased overall attendance that prompted at least 50,000 new visits in 2011. All by putting the power of analytics in the hands of the end user.

How Does Upgrading Help Your  ‘Zoo’?

The ability to empower end users makes sense when the user interface is easy to use and allows for quick adoption. Cognos V10 has a more streamlined interface that is easier to navigate, making it more readily adoptable for end users who want to simply access and understand the data.

Another major component is collaboration between co-workers and other departments. Cognos V10 incorporates collaboration within the interface, so that threads are maintained and data is kept within the platform, rather than having to utilize a third-party system for collaboration and communication.

“End users don’t stay put, so mobility is key, particularly if the focus is to capture and analyze data real-time,” said Marti. “With Cognos V10, users can literally analyze on the go with systems that work whether they are connected or disconnected.“

Even with the mobility and dynamic analytics functions, the system has very fast response times and handles very large data volumes. Using Dynamic Cubes increases the performance of the tools and allows for end users to slice and dice data with a fast response time, keeping them happy and focused on analyzing data.

Make Better Business Decisions – in Real Time

By putting analytics in the hands of everyone, businesses will benefit from having information in the hands of those best suited to take action. Cognos Business Intelligence allows for integration with the next logical step – predictive analytics. By adopting business prototypes quickly, companies are now able to build out corporate solutions faster than before. “What If” and scenario modeling help determine next appropriate steps giving the business a more prescriptive solution.

In Your Corner for the Cognos Upgrade

Of course, the next logical question is how to tackle the upgrade. While the benefits are certainly proven, tackling an upgrade of any type requires thought and process to ensure the most seamless approach is taken. That is where NEC Analytics team can help.

NEC is largest global systems integrator, and has more than 50 years of experience in the North American market alone. We leverage our rich history coupled with partnership experience with IBM and others to ensure a positive outcome.

Our analytics team focuses on Performance Management, Integration, Total Performance Outsourcing (which includes design, deployment and management) and Smart Predict Solutions designed to meet the specific needs of our clients (you can learn more about our analytics solutions in this short video).

NEC specializes in supporting Cognos and is offering a complimentary assessment to ensure that all your business needs are captured to create a seamless upgrade. Contact us to schedule your assessment or visit our Analytics Center of Excellence for more information.

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NEC’s Analytics Portfolio

Data is plentiful. Yet without the right support team, that information is lost potential. Achieve your business intelligence objectives with NEC analytics Click To TweetOur field-tested analysts and best-in-class technologies can steer you in the right direction, keep you on the right track and help you outpace the competition with actionable insight that delivers the most value and best suits your company’s decision-making ambitions and processes. Learn more today by scheduling your complimentary assessment.

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No Jitter Interviews Larry Levenberg at Enterprise Connect 2015

In this interview, Larry Levenberg discusses NEC’s presence at Enterprise Connect, the impact of Big Data on companies and NEC’s expertise in Big Data and Analytics as well as where NEC is headed.

An Interview with Atsushi Kitazawa of NEC Japan, the “Father” of IERS

Everything you wanted to know about IERS, from its position in the world of next-generation databases to its design goals, architecture, and prominent use cases.

I recently got the chance to talk to Atsushi Kitazawa, chief engineer at NEC Corporation, about the company’s new InfoFrame Elastic Relational Store (IERS) database.    I enjoyed the discussion with Kitazawa-san immensely – he has an ability to seamlessly flow from a deep technical point to a higher-level business point that made our talk especially informative.

Matt Sarrel (MS): Where did the idea for IERS come from?

Atsushi Kitazawa (AK): We decided to build IERS on top of NEC’s micro-sharding technology in 2011. The reason is that all of the cloud players see scalability and consistency as major features and we wanted to build a product with both. Google published the Google File System implementation in 2003 and then they published Bigtable (KVS) in 2006. Amazon also published Amazon Dynamo (KVS) in 2007. NEC published our CloudDB vision paper in 2009, which helped us to establish the architecture of a key value store under the database umbrella. In 2011, Facebook published improved performance of Apache Hadoop and Google published the method of transaction processing on top of BigTable called Megastore BigTable. Those players looked at scalability and then consistency. By 2011 they had both.

A KVS is well-suited for building a scalable system. The performance has to be predictable under increasing and changing workloads. At the beginning, all the cloud players were using replication in order to increase performance, but they hit some walls because of the unpredictability of caching. You cannot cache everything. So they moved to a caching and sharding architecture so you can partition data to multiple servers in order to increase caching in memory. And then the problem here is that it is not so easy to shard a database in a consistent manner. This is the problem of deep partitioning. You can see the partitioning or sharding in the beginning—it is not so difficult–but dynamic partitioning and sharding is very difficult. The end goal of many projects was to provide a distributed KVS. The requirement of a KVS is predictability of performance under whatever workload we have.

MS:  Why is a KVS is better? 

AK: The most important thing about a KVS is that we can move part of the data from one node to another in order to balance performance. Typically, the implementation of a KVS relies on small partitions that can be moved between nodes. This is very difficult when you consider all of the nodes included in a relational database or any database for that matter. In a KVS, everything is built on the key value so we can track where data resides.


Going back to the evolution of database products, Facebook developed Cassandra on its own because it needed it. It had to move part of the application from Cassandra to HBase but had to improve HBase first. Facebook reported in a paper the reason why it had to use HBase is that it need consistency in order to implement its messaging application. The messaging application, made available in 2011, enabled users to manage a single inbox for various messages including chats and Tweets. This totals 15 billion messages from 350 million members every month and 120 billion chats between 300 million members. Then Facebook wanted to add consistency on top of performance because of the increased number of messages delivered.

On the other hand, Google added a transactional layer on top of its BigTable KVS. It did this for the app engine that is used by many users concurrently. The transactional layer allowed users to write their application code.  Google also developed Caffeine for near-real-time index processing and HRD (High Replication Datastore) for OLTP systems such as AppEngine to use.

Those are the trends that cloud players illustrated when NEC was deciding to enter this market. At NEC we developed our own proprietary database for mainframe moret han 30 years ago. Incidentally, I was on that team. We didn’t extend our reach to Unix or Windows so we didn’t have a database product for those platforms. In 2005, we decided to develop our own in-memory database and made it available in Japan. This is TAM or transactional in-memory database. We added the ability to process more queries by adding a columnar database called DataBooster in 2007. Now we have in-memory databases for transactions and queries. In 2010, we successfully released and deployed the in-memory database for a large Japanese customer. As our North America research team released the CloudDB paper, we merged the technologies together to become IERS.

We felt that if we could develop everything on top of a KVS, then it would be scalable. That is a core concept of IERS.

MS:  What were the design goals of IERS?  Could you describe how those goals are met by the system’s architecture?

AK: Regarding our architecture, the transaction nodes implement intelligent logs with in-memory database to facilitate transaction processing. The difference between IERS and most databases is that IERS is a log system machine. IERS does not have any cache (read, dirty, write) and this means we don’t have to synchronize cache in the usual manner. We just record all the changes to the transactional server in time order fashion and then synchronize the changes in batches to other data pods over IERS, which are database servers. The result is that the KVS only maintains committed changes.


We do have a cache, but it is a read-only cache, not the typical database cache. The only data the cache maintains is for reads from the query server. We do not need to be concerned with cache coherency. The transaction server itself is an in-memory database. We record every change on the transaction server and we replicate across at least three nodes. The major difference between IERS and other databases is the method of data propagation. Our technology allows the query server, accessible via SQL, to see a consistent view even though we have separate read and write cache. If you do not care much about consistency, then you can rely on the storage server’s cache. The storage server consists of the data previously transferred from the transaction server. If you consider the consistency between each record or each table, then you should read from the transaction server so that we maintain the entire consistency of the transaction.

The important point in terms of scalability is that both the KVS (storage) server and the transaction work as if they are KVS storage so we can maintain scalability as if the entire database is a KVS even though we have a transactional logging layer.

From a business point of view, there are users who are using a KVS such as Cassandra, which does not support consistency in a transactional manner. We want to see those users to extend their databases by adding another application. If they want a KVS that supports consistent transactions then we can help them. On the other hand, in Japan we see that some of our customers are trying to move their existing applications from RDBMS to a more scalable environment because of a rapid increase in their incoming traffic. In that case, they have their own SQL applications. Rewriting SQL for a KVS is very difficult if it doesn’t support SQL. So we added a SQL layer that allows users to easily migrate existing applications from RDBMS to KVS.

MS: Is there a part of IERS’ functionality or architecture that makes it unique?

AK:  From a customer point of view the difference is that IERS provides complete scalability and consistency. The key is the extent that we support the consistency and SQL to make it easier for customers to run their applications. We added a productivity layer on top of a pure scalable database. We can continue to improve the productivity layer. Typically, people have to compromise productivity to get scalability. Simply pursuing scalability isn’t so difficult. Application database vendors focus on the productivity layer. Then they add scalability. Our direction is different. We first look at scalability. We built a completely scalable database. Then we added the productivity layer – security support, transactional support – without compromising scalability.

MS: What types of projects is IERS well-suited for?

AK: Messaging is one good application. If you want to store each message in transaction fashion (track if it goes out, if it’s read, responded to, etc.) and require scalability, then this is a good application for IERS.

Another case is M2M because it requires scalability and there is usually a dramatic increase over time of the number of devices connected. The customer also has a requirement to maintain each device in transaction fashion. Each device has its own history that must be maintained in a consistent manner.

To learn more about NEC’s IER’S solution visit:

Matt Sarrel *Matt Sarrel is a leading tech analyst and writer providing guest content for NEC.