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An RX For Hospitals

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ProgrammableFlow® Wins Management Category...

More cores. More threads. More memory. More I/O. 2X the performance.

Introducing NEC's new Express5800 Enterprise Server...

NEC and Microsoft

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UNIVERGE® SV9000

Empower the Smart Workforce ...

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AFIS Internet Conference 2014

Salt Lake City, Utah ...

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An RX For Hospitals

Can a new telephone system save lives? ...

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Best of Interop 2104

ProgrammableFlow® Wins Management Category ...

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More cores. More threads. More memory. M

Introducing NEC's new Express5800 Enterprise Server ...

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NEC and Microsoft

Delivering Open, Standards-Based SDN for Cloud ...

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When it comes to football, everyone has a favorite team. The fans not only cheer their hearts out, but they feel as if they are an integral part of their team. Well, now thanks to big data, fans will feel like they are an even bigger part of the game!

Have you ever found yourself yelling at the television, trying desperately to tell the players what their next critical move should be? With the emerging advancements of big data and analytics, armchair quarterbacks will have up-to-date information and statistics at their fingertips. And it’s coming to you this season!

Big Data enters the NFL

Photo by AJ Guel on Flickr and used here with Creative Commons license.

The National Football League (NFL) announced that in-game player tracking technology will be available for the 2014 season in 17 stadiums. Using RFID tracking chips on every player, the NFL will now be able to measure player orientation. This initiative, called “Next Generation Statistics,” will provide insights that will be available primarily on Thursday Night Football games shown on the NFL Network.

The implementation of RFID tracking chips will provide a statistical link between the players and the fans. The technology measures distance within motion-based systems. These chips will track players’ movements and positions wherever they are on the field. Talk about an interactive fan experience! This level of “Next Generation Statistics” occurs in real-time and provides a wide-range of data types which include:

·         Precise positioning data

·         Velocity

·         Acceleration

·         Run distance

·         Impact measurements

This data can be collected, processed, and presented to the audience in a matter of seconds, ultimately enhancing the football experience. It makes you wonder…could this information potentially create a more perfect game of football?

The players and coaches will have the ability to see exactly what occurred in any particular play, and giving them information to make the necessary changes for improvement. No more questions about who made a mistake because information will be broken down into statistics that can be analyzed and dissected by coaches and fans alike. It will make for an exciting, interactive 2014 season for sure!

Big Data – Most Valuable Player                                                                                        

The NFL initiative is following both the NBA’s and NASCAR’s foray into using big data to augment the fan experience. In the case of the NFL, fans will have access to statistics previously unavailable, but also broken down in a spatially oriented way so the fan can actually understand what happened, to whom, how and where.

Converting huge volumes of collected data transforms the information into a meaningful set of numbers, revealing unexpected patterns and correlations. This provides an opportunity for fans to be more informed, as well as coaches and players who can make better decisions going forward.

However, just like any winning team, big data needs a quarterback. In this case, it would be analytics. Frequently we talk about big data as a singular “event” where data comes in and then back out as a usable source of information. However, without analytics, big data is well, just large amounts of data.

The winning team of big data and analytics has been a focus for NEC for some time. The combination helps make sense of the abundance of available data that reside within the enterprise and transforms it into trustworthy insight and advanced analytics for sustainable intelligence.

For the NFL, the use of big data and analytics will provide new perspectives from a variety of viewpoints right after the play takes place. Now, the game will be viewed from the inside out and millions of eyes will not only be watching, but they will be in on the action as well.

The technological advances of big data and analytics are providing us a more in-depth glimpse at the world we live in. Experiences that could never be fully understood before are now being displayed in detail through real-time big data and analytics.

To learn more about big data and analytics and how they can be used in your industry, check this infographic provided by NEC and Aberdeen Group - Predictive Analytics and Big Data: A Powerful Combination.


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NEC Corporation of America (NEC) is sponsoring a meet-up in Sunnyvale, CA, on Sept. 11, featuring a panel of industry experts discussing how to leverage technology and innovation for proactive healthcare.

Healthcare providers are increasingly under budgetary pressure to reduce costs and increase the efficiency and quality of care.  Preventive treatments free up costly hospital resources.  Technology is proving to be a key enabler in realizing these goals, especially as healthcare delivery models are evolving rapidly.

To improve wellness worldwide, innovators from across healthcare are envisioning and realizing game-changing digital health technologies.  Come hear real-world approaches, first-hand experiences and lessons learned from trailblazers on the leading edge of healthcare.

Details of the meet-up:

When:  Thursday, September 11 from 6 - 8:30 p.m.

Where:  Plug and Play Tech Center

Address:
440 N Wolfe Rd
Sunnyvale, CA 94085

Register for this event.

Moderator

Charlene Yu Vaughn, CEO, The Algonquin Group

Panel of Experts

  • Dr. Andrew Auerbach, MD, MPH, Director of InnovaEon at the Center for Digital Health InnovaEon; Professor of Medicine in Residence at UCSF
  • Jason Roos, CTO, Stanford Medical Center

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If you stop to think about it, the relational database (RDBMS) is a pretty remarkable piece of technology. Can you name another product category that has remained essentially unchanged since it was first introduced roughly 40 years ago?

However, in 2014 the RDBMS is no longer the “be all and end all” of database technology. An RDBMS can’t meet the demands placed on it by big data and cloud computing. Data entry has changed dramatically also. Instead of a requirement to scale with the number of data processing employees, there is now a requirement to scale with the number of customers, or to give a more dramatic example, to scale with the number of devices or sensors in a machine-to-machine (M2M) or Internet of Things (IoT) scenario. Many enterprises have outgrown their RDBMS, as have most telecommunications providers.

All up and down the application stack we can see the ability to scale out and in quite easily. By definition, big data requires an elastic database that can scale across multiple storage and compute nodes. Other technology that was created to be elastic, such as NoSQL and NewSQL, are more appropriate for big data environments.

How to Know if You Need Elasticity

Not every project requires an elastic database. When one does, things go much more smoothly if you can figure that out in advance and plan accordingly. It’s much easier if you make the effort to plan for an elastic architecture from the beginning. Look at your planned load and your projected growth and ask yourself whether it will exceed the capacity of your current hardware/software architecture. Will your database requirements fluctuate in demand? Will there be daily, weekly, monthly, and/or seasonal changes in the number of servers required? For example, if you have an analytics application that requires eight database nodes 24x7, yet peaks at 14 nodes for four hours every night, then elasticity is important.

NoSQL, NewSQL, and Elasticity

When NoSQL systems were initially being designed, emphasis was placed on scalability. In many cases, this meant eliminating many of the features that had been added to RDBMSs over time: powerful query languages, database consistency guarantees, durability, atomic operations – and just about everything else. At this point we had lots of elastically scalable databases that offered little else, whereby making them unusable except for very specific use cases.

NewSQL goes beyond NoSQL and stipulates that elasticity isn’t the only thing that matters and is instead a baseline requirement. Many of the things that we gave up (such as SQL) in our quest for elasticity are important. NoSQL required many of these things to be built into applications (increasing complexity and cost) and now we want them back in the database layer where they belong.

IERS is Built for Elasticity

In addition to having elasticity in its name, NEC’s InfoFrame Elastic Relational Store (IERS) was designed from the very beginning to provide a high-performance elastically scalable database with full ACID capabilities. IERS’ scale-out architecture expands your system without downtime as demand and data volume increases. This allows you to start small, save on unnecessary resource investments and then scale out easily based on demand. Minimal to no application modification is required to scale out or in.

IERS is Built for Elasticity

IERS can scale out easily and quickly. System resource can be added while the system is live and in production, enabling the system to be reconfigured on-the-fly without downtime. Also, as the system scales out, automatic rebalancing of the data takes place. This process does not impact user operations. IERS sports an easy to use web based GUI that allows administrators to scale-in/scale-out with a few clicks from anywhere in the globe. Process once initiated requires no further human intervention.

To learn more about NEC’s IERS solution visit:  http://goo.gl/TnFkbR

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


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In my last blog post I provided a general introduction to key value stores (KVS). In this post I’m going to explain how InfoFrame Elastic Relational Store (IERS) takes the basic concepts of the KVS and improves upon them to build a database with strong business oriented features.

The main improvement is that IERS is built to process high-speed transactions with full ACID capabilities. As a quick refresher, ACID stands for Atomicity, Consistency, Isolation, Durability and refers to the set of properties that deliver reliable processing of database transactions. Atomicity preserves transaction integrity by only allowing complete transactions to be committed, not just parts of transactions. Consistency allows for well-defined rules to control and validate the data before it is written to the database. Isolation verifies that concurrent execution of transactions still results in complete transactions being committed; this is where conflict resolution takes places. Durability means that once a transaction has been committed it will remain intact even in the event of power loss, crashes, or other system errors. IERS offers full ACID support and thus meets the requirements for a business environment processing transactions, which many KVS fail to meet.

Most KVS cannot guarantee the constraints that developers need to place on data in order to preserve consistency. Consistency needs to be handled by the application, which pushes this critical function further away from the database engine and makes it more cumbersome to design the application as the application must now include features the database should handle. On the other hand, IERS provides consistency at the database layer, preserving the high performance of KVS.

Whereas most KVS require database-specific code to be written, IERS uses an industry standard SQL interface. Most KVS platforms don’t support SQL, hence the name NoSQL being used as a term to describe them. Over time, NoSQL as a term has expanded to include next-generation databases with a SQL interface by rebranding itself as “Not Only SQL.” Some industry analysts refer to next-generation databases with a SQL interface as NewSQL. However, the most commonly used database programming language is SQL and many business environments already have a significant investment in SQL. For these reasons, SQL support is one of the most important and most used features of IERS. Using another KVS might require developers to learn a custom API, thus delaying the development process. IERS, with its SQL support, allows developers to get up and running as fast as possible.

Database security is also a requirement in a business environment. IERS provides the same user authentication and table level access management as an RDBMS. In contrast, the typical KVS will push this up to the application layer. IERS also offers full support for user activity logging and can be integrated with a solution like IBM Guardium to provide complete audit trails.

IERS also fully supports range queries, a common database operation that retrieves all records where some value is between an upper and lower boundary. For example, list all customers between ages 8 and 18. A typical KVS cannot support a range query. In fact, a typical KVS only supports queries on the key.

As you can see, IERS contains many enhancements that are typically not found in a KVS. When the database layer lacks such functionality, then it must be implemented in the application layer. This requires the application layer to manage transactions, security, data constraints and consistency. It’s much easier to simply use a database that contains this functionality such as IERS. By including the functionality described in this posting, IERS demonstrates that it is more applicable for use in solving business problems than a typical key value store. The most important of these enhancements is full support for ACID transactions; without ACID there cannot be transactions. Businesses evaluating NoSQL and NewSQL key value stores for a high-speed transaction driven environment will find that IERS more than meets their needs.

To learn more about NEC’s IERS solution visit:  http://goo.gl/TnFkbR

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


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Key Value Stores are perhaps the most common form of NoSQL and NewSQL databases.  They consist of (surprise!) keys and values and are built from the ground up to store and retrieve these values as fast as possible.  For this reason, a KVS is considered an excellent way to store and retrieve information for high-traffic web sites and other high-performance content, but not the greatest for transaction-driven projects.   According to DB-Engines, key value stores are one of the more popular non-RDBMS databases in use.

Structurally, KVS are the most straightforward of the NoSQL databases and this basic underlying factor accounts largely for why they are so mind-bogglingly fast.  The beauty of a KVS is its simplicity.  Instead of worrying about complex schema and data relationships (as with a traditional RDBMS), a KVS just has to store and retrieve values linked to a key.  The most commonly implemented KVSs include Redis, Riak, and VoldemortNEC’s IERS is built on top KVS with many added enhancements.

It’s easier to understand a KVS if you first look at a traditional RDBMS.  Think of this as a structured and table-based database.  For example, if you’re working with employee data, you’d have a table with columns for each field you wanted to track and a row for each user.  It would look something like this:

ID

First Name

Last Name

1

Homer

Simpson

2

Marge

Bouvier

3

Herschel

Krustofsky

The table approach works well if you have a reasonable number, a few dozen to a few thousand, of people to track.  It also works well if you can do your queries off-line where speed isn’t an issue, and can do your batch processing for reporting at off hours because those reports will take a considerable amount of time.

However, in the big data world we don’t have the luxury of running queries and reports during off-hours.  Whatever it is, in the big data world we need it now.  Not only that, the traditional table shown above may become a big management mess when it’s too big to fit on a single server. Taking the example to a KVS, imagine that you’ve got users instead of employees.  Now you’re talking about millions of records instead of thousands, and they need to be available quickly from around the world 24/7.  When a user logs in, he wants to be able to have instant access to his account.  Plus, not every user record has every bit of information as every other record; some users may provide their phone numbers, some may not.  Each record potentially has a different length and different values.

To store and retrieve this kind of data quickly, you generate a key for each record and then store whatever fields (what would have been columns in the table above) are available.  Each field is comprised of a data name and the data itself.  If you don’t have a particular piece of data, instead of leaving an empty cell in a table you simply don’t store the data name / data combination.

Let’s take a look:

Key: 1

ID: HS

First Name: Homer


Key: 2

Email: This e-mail address is being protected from spambots. You need JavaScript enabled to view it

City: Springfield

Age: 34


Key: 3

Twitter ID: @hkrustofsky

First Name: Herschel

Occupation: Clown

As you can see, users can log in using ID, email, or Twitter ID.  This simply wouldn’t have been possible using a traditional table style RDBMS.  Also, queries need to be built around keys because there are no field (or column) names.  There’s no need to pull data from multiple tables, reformat it and import it into another table just so users with different information stored can log in.

NEC’s IERS takes advantage of the straightforward nature of a KVS.  I blogged about this a few weeks ago when I posted coverage of my interview with Atsushi Kitazawa, the “father” of IERS.  Due to the nature of a storing multiple values associated with a unique key, distributed KVS performance is predictable.  A KVS is usually partitioned to run on multiple nodes.  Because each key is unique, all values associated with a key, regardless of where the values are physically located, are equally accessible. 

So there you have it, an explanation of KVS’s and how they work.  While a KVS forms the foundation of NEC’s IERS, there are plenty of enhancements that take IERS above and beyond what the average KVS is capable of.  In particular, IERS provides a high-performance and consistent environment with transparent scaling for transactions.  My next posting will discuss these advantages and how to make the best use of them when developing for IERS.

To learn more about NEC’s IER’S solution visit:  http://goo.gl/TnFkbR

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


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