JVMKiller

June 22nd, 2010 by Jim Fleming

In our latest release of SMB, we spent a fair amount of time testing and minimizing windows where data corruption can occur in our associative memory.  The testing involved killing various processes during periods of activity.  But rather than a hit-or-miss approach of randomly killing SMB processes and seeing if any sort of recovery was needed, we decided to build a JVMKiller that gave us control of when and how the JVM goes down.  We are now able to target specific “high risk” (write operations) areas.  Like everything else in our system, the JVMKiller is configured as a JavaBean.  The bean has the ability to execute any number of possible kill events, as well as, targeting both specific logical and physical components of the system, given a condition.  For example, we could target a certain event, like the start of a phase-2 save event:

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SaffronSierra & Gmail Classification

April 30th, 2010 by admin

I’ve recently added some sample code to our “examples” repository that demonstrates how to use SaffronMemoryBase running on SaffronSierra to do basic email classification. The example leverages the convenience of labels within Gmail to provide the “labels” for classifying future emails.

If you have a Gmail account (or a Google Apps account) then you already know that as emails come in you can associate them with labels. You might have labels such as “accounts”, “soccer”, “music”, “work”, etc… (those are some of mine anyway). As I started thinking about building an email classification example it occurred to me that the labels within Gmail would provide a nice, easy way of doing classification. By “labeling” emails in Gmail I’ve already made the statement, “This email is about work”, or “This email is about soccer”. Why not leverage that hard work?

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SaffronSierra Refresh

April 16th, 2010 by admin

We just pushed out a minor refresh to SaffronSierra. As a user you won’t notice much that’s different (hopefully). This refresh brings with it a couple of changes. One that users will see, and another they won’t. Let me talk about both briefly:

  • Nightly Running Reminders – SaffronSierra is a pay-as-you-go service. You pay for every hour your service is running. SaffronSierra makes it really simple (you click a button) to start a SaffronMemoryBase system. It’s so simple in fact that it’s easy to start it and forget that it’s running. Now SaffronSierra will send email reminders every night to users who’s SaffronSierra service is running. If you actually want your service to be running 24/7 (which is totally fine by the way), and you don’t want to receive these reminders you can login and update your user profile.
  • New Amazon AWS Java Libraries – SaffronSierra uses Amazon AWS as it’s backend infrastructure for running services. The java library we originally used to integrate with AWS has been deprecated. So, we ported SaffronSierra over to the new java library. This actually was a pretty painless process. There are some differences between the two libraries, but as I told someone recently “it’s different in the ways that it needs to be different.” Hopefully you won’t notice this change and things just keep humming along.

We’ll keep the updates coming, stay tuned.

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TweetDive Source Code

April 14th, 2010 by admin

I’m happy to announce that we’ve made the TweetDive source code publicly available (finally). I know a number of people have been waiting to get their hands on it. For details on how to grab it from Subversion take a look at this page.

We hope this code will provide a quick-start for developers getting started with SaffronSierra. TweetDive uses a number of different REST APIs available on SaffronSierra. You can use it as a reference as you build your own stuff, or you can extend TweetDive with new features. If you’d like to see your changes incorporated into the running version of TweetDive please contact us. We’d be happy to incorporate patches.

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Partnering with Saffron

April 12th, 2010 by Gayle Sheppard

We’re hearing it every day:  Customers want better, more advanced analytic capabilities.

Customers are telling us they want to unify and apply advanced data analytics to  disparate data sources –– semantic web, enterprise data, sensor data, structured and unstructured data, for example –– to improve business decisions and results.

It’s music to our ears because that’s what our associative memory technology does: We provide “beyond BI” capabilities, in a single tool, for sense-making and decision support.  We can help our customers make better sense of things and make better decisions because we expose all the relevant experiences –– connections, similarities, prior outcomes for example –– across all the data in real time.

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Updated Pricing

April 7th, 2010 by admin

Starting today we’ve changed the way pricing works for SaffronSierra. Hopefully you will find our new approach much simpler. What is our new approach? Some would  call it “pay-as-you-go”. Quite simply there is a per-hour rate based on what type of SaffronSierra cluster you’re running. The per-hour rate starts at $1.00 an hour and goes up from there. There is no commitment, we just bill you every month for the time you use. If you haven’t used your service then you won’t be billed. Pretty simple right?

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The Big Red Easy Button

March 30th, 2010 by Jim Fleming

Better known as SaffronSierra.  Today Saffron announced the availability of SaffronSierra, a cloud delivery of the SaffronMemoryBase platform.  As you know, SaffronSierra allows developers a quick and easy way to provision an associative memory “data service” with a push of a button.  How easy is that?  SaffronSierra will soon become even easier with a “no commitment, charge by the hour” payment plan, as well as, the ability to scale your service up to handle very large data volumes.  If you are curious about the associations you might find in your data, now is a good time to try SaffronSierra, you have nothing to lose.

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REST API Announcement

March 23rd, 2010 by Jim Fleming

Today Saffron announced our REST APIs for sense-making and decision support applications using SaffronMemoryBase (SMB).  With these REST APIs, we’ve taken an otherwise difficult subject and made it easy.  Similar to how REST has simplified SOA, Saffron’s REST APIs have simplified analytics.  Notable features of our API are:

  1. Powerful, yet simple to use.  For example, to ask what products are connected to the company saffron in the context of announcement, would be as easy as HTTP GET: http://hostname/ws/spaces/default/connections?q=saffron,announcement&c=product
  2. Includes both administration and application APIs.
  3. Includes both ingestion (write) and query (read) APIs.
  4. Queries cover everything from low-level (e.g. returning a sub-matrix of association counts) to high-level (e.g. returning rank-ordered list of association trends) operations.
  5. The API is not overwhelming.  There are a small number of APIs that can be applied different ways to solve many problems.
  6. Dynamically combine associations together in various ways at query time.  SMB’s virtual attribute system even allows the system to “see” multiple attributes as one, providing a way to fix-up data cleansing errors (e.g. Bill and William are really the same person).
  7. Standard JSON/XML input/output formats.
  8. SMB’s performance and REST API is ideally suited for today’s new class of analytical applications.

For more information about Saffron REST APIs please visit SaffronSierraSaffron Technology or view today’s news announcement.

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Email Filtering: A Case for Fast and Easy Learning

March 11th, 2010 by Manny Aparicio

Early in Saffron’s life, a couple of guys, Bob Cagle and Dean Pfutzenreuter from Open Field software on the West Coast, understood the power of our associative memories and applied it to spam filtering.  Beyond just spam, their Electronic Learning Assistant (ELLA) was a personalized email management system for any set of folders defined by each user.  As reviewed in PC User Magazine when compared against many other solutions including collaborative filtering and a naïve Bayesian, ELLA was declared “World’s Best Spam Blocker”.  It was similarly praised by other reviews in Forbes and Fortune and by end users as “near perfect”.   As one user wrote, “[I] Have been using Ella for a few days. But, after only two mistakes so far, Ella has been 100%.  As they say, it learns as it goes. Seems to be the case.”

ELLA proves the incremental, non-parametric, nonlinear learning of associative memories.  Any user – even a cave man – can simply create a folder, show ELLA a few examples though a wizard interface, and it is off and running.  No “black art” parameter tweaking.  Learning on the fly.  Add new folders at any time or teach it new cases as spam attackers also change their tactics.  By simply correcting it when it makes a filing mistake, it keeps tracking to near perfect.  This is due to the highly nonlinear representation of the memories but also to the brilliance of Bob and Dean in how they represent email.  Bob and Dean are shining examples of our customers and partners as “creative entrepreneurs”.   Given our nonlinear engine to reason about attribute interactions, they developed the 100 email attributes that also served to make ELLA so accurate.  The power of the engine was combined with the brilliance of application design.

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CL-EC2, a Common Lisp Interface to Amazon’s EC2 Query API, Now Available

February 23rd, 2010 by David Young

Version 0.1 of CL-EC2 is now available for download, under an MIT-style license. The project is hosted at common-lisp.net, and may be found here. There are several mailing lists available, and contributors are welcome.

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