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Falkonry

Repost: Deloitte University Press: Industrialized Analytics

By | IT/OT Management

This post from Deloitte University, first published in February of 2016, discusses ‘data as the new oil.’ It’s a great article that underscores the significance that data can contribute to an organization’s agility and ultimately, competitive position in the market.  It also, as they state, is the foundation to the digital transformation that industry is now undergoing.

Think back just a few years if you are in manufacturing.  The #cloud, #Internet of Things, even #virtualization were far-off concepts that had little to no relevance in your world. Your goals–keeping your operations running efficiently, avoiding downtime and unexpected conditions–were paramount to your GMP (good manufacturing practices) and digitization mostly meant taking real-time process data from a DCS (Distributed Control System) or PLC (Programmable Logic Controller) and using it with a data historian for process control.  The influx of applications such as enterprise manufacturing intelligence, dashboards, and other ‘big data’ systems brought a step change to the way you understood your operations, and the cause/effect of events.   Being able to now isolate, or predict, events was huge, and data (and the effective use of it) was the reason for this efficiency.

Now, another step change is occurring. Advanced techniques such as Artificial Intelligence, Machine Learning and Pattern Recognition offer insights into behavior of the assets, people and processes.  Data becomes not only oil, it’s gold.  It’s the key to your understanding and efficiency.

Falkonry is one of those applications riding the data wave; a window into your process behavior using your resident subject matter experts, along with the data, to provide context and relevancy to that data.   See more about Falkonry pattern recognition here.

 

 

Falkonry Listed in Top 125 Startups in Industrial Internet of Things!

By | IT/OT Management

Falkonry thanks @CB Insights for its inclusion into the Industrial Internet of Things Top 125 Startups.  We’re in the Advanced Analytics, Edge Intelligence and Protection category.

Falkonry accelerates continuous improvement of production operations through intelligent pattern recognition/AI, to increase uptime, quality, yield.  It does this by connecting to the real-time data that exists within an enterprise–be that from an edge device, or from an enterprise application, like ERP, MES or PLM, and supplementing legacy process control applications such as a PLC, DCS or SCADA system. Together, we’re better!  For more information on how Falkonry helps an IIoT architecture, please visit Falkonry Pattern Recognition for IoT.

Solution fit and flexibility: why Falkonry product teams invest time in redesigning their UI

By | IT/OT Management

Solution fit and flexibility: why Falkonry product teams invest time in redesigning their UI

A series on product improvements at Falkonry – Part 1

By Jeegar Shah – Sr. Director Products,  Falkonry

 

When designing a complex product that has all the bells and whistles under the hood, it is imperative that the user experience be all the more simplistic, streamlined and intuitive.

As any product evolves to encompass new ideas and optimizations, there is a tendency to display the prowess of your algorithmic horsepower to your users by giving them YAF (Yet Another Feature). This is all dandy for developers that have an exploding cache of Github repos and code checkins as they try their one upmanship on their colleagues with cumulative pull requests. But this is often a nightmare for customers who are asymptotically approaching their steady state of product usage and now have to deal with YAF – when they never really asked for one.

At Falkonry, our overarching objective is solution fit and flexibility. We believe that the promise of bringing OT (Operations Technology) efficiency lies in the ability to empower subject matter experts (SMEs) with the tools to make timely and effective decisions. A YAF may exactly help you achieve the opposite. Falkonry is committed to making AI-based pattern recognition an easy-to-incorporate component of any operations-oriented solution. With the addition of every algorithmic richness and IP, we enforce a mindset of looking back at our footsteps to remind us of the silhouette of an army of SME’s that will be following the same steps and pausing every so often to ask each other the question “Did we just throw a YAF?”

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Visual Goodies in Falkonry’s Learning

By | IT/OT Management

The learning capabilities of Falkonry can be pretty addictive because they are so visually gratifying. Now to round out that Learning & Monitor interface, we added a few nifty features.

Improved Condition Summary

Increased visual capabilities

 

You can now quickly find out which elements are in a given condition(s) in a given period of time, so that you can navigate directly to those events. A dog-eared pull-out is available on all summary blocks so that you can see which conditions occur in that block of time and which events are in that condition, and for how much of the time. You can select the events you want to investigate further so that you can quickly assess the findings of a condition in a period of time.

This new feature will enable you to evaluate how conditions arise across your data set and what patterns are discovered by Falkonry in that data. When dealing with large numbers of things this feature will improve your ability to inspect Falkonry’s findings without losing context of the overall problem.

Improved Condition Buttons

This works in combination with the condition selections, which we have further simplified by reducing all unlabeled conditions to a circular buttons and user-named conditions to rounded corner rectangular buttons. Additionally, you only see buttons for conditions you are currently looking at. You can always bring in additional condition timelines from the selection panel by choosing old classifications or new verification, etc.

There are other usability improvements that provide you the information you are looking for, removing delays such as:

  • Increased the amount of vertical screen space available for viewing by removing the fixed navigation bar
  • Reduced the lag between creating a pipeline and being able to access the pipeline for learning
  • Added the ability to inspect your data just before starting the learning process
  • Auto-updating of the histories of flow segments and learning in the Configuration view

Analytics Performance Improvement

On top of all these visual improvements, the speed of the analytics was improved so that your model revisions complete faster and with fewer and more understandable errors.

These improvements are iterative steps we’ve taken to increase performance and interoperability. We’ll continue to work on increasing connections to a variety of data sources, providing greater manageability of pipelines, as well as improving the learning and monitoring interfaces. All of this is to improve reliability, performance and intelligence.

This post was originally written March 6, 2016

Falkonry Service Improves the AI Experience: More Connections, Better Deployment Options

By | IT/OT Management

Falkonry Service was introduced a few months ago to improve the Falkonry solution fit and flexibility. As we’ve interacted with customers over the last several months, we’ve gained a greater understanding of the many different types of solutions people are trying to build with embedded pattern recognition capabilities provided by Falkonry. This release is a reaction to those needs and includes:

  • Improved data consumption capabilities
  • Expanded connection options
  • Simpler private deployment options

Falkonry Service Architecture

 

You can watch videos about Falkonry on our Website to learn more.

Improved data consumption capabilities

To better address current needs and in anticipation of future needs, we added a new core architectural element to Falkonry called Event Buffer.  Event Buffers separate the responsibility for managing data inbound to Falkonry from Pipelines that process that data.  One obvious benefit from the addition of Event Buffers is that one data source can supply data to multiple pipelines. This capability can be used to simply allow reuse of previously loaded data or to support more complex real-time simultaneous pattern recognition scenarios. Each pipeline, for example can make independent choices on how to interpret and process the same data stream.  An additional capability associated with Event Buffers is the ability to chain Pipeline executions to each other – i.e. to route output from one Pipeline to an Event Buffer that feeds other Pipelines.

Event Buffers also support and provide a focus for a growing set of capabilities related to data consumption.  The new release, for example, allows users to supply data to Falkonry in ‘Data Historian style’ format – sets of points in the form of a <timestamp, tag, value> triple format.  This augments the tabular structure supported previously.  Likewise, JSON (line delimited) support was added to complement CSV.

Expanded Connection Options

The new release also makes it easier to connect to Falkonry and to embed it in your solutions.  New capabilities include:

  • MQTT connectivity
  • Webhooks connectivity
  • Updated REST API
  • Expanded set of client libraries
  • Updated Splunk plugin

The Falkonry UI makes it easy for an Event Buffer to subscribe to a MQTT topic or for a Pipeline’s outflow  to be published to a MQTT topic or a Webhook URL. Additional subscription and publication options will be added in the future.

Client libraries are now available for Javascript and Python and others (e.g. C# and Java) are under development.

The Falkonry Splunk App that makes it easy to bi-directionally connect Splunk to a Falkonry Service instance was updated and streamlined, and plug-ins for other data platforms are under development.

Simpler Private Deployment Options

While the Falkonry Sandbox provides a useful and effective path for experimentation and early solution development, private deployments are the primary way Falkonry gets delivered.  Private deployments can be either:

  • Virtual Private Cloud (VPC) Deployments: Falkonry deployed in customer specific VPCs on public provider infrastructure like Oracle, Microsoft Azure, Google Compute Engine (GCE), Amazon Web Services (AWS)
  • Private Deployments: Falkonry deployed on customer controlled compute and storage infrastructure.

To make installation much simpler, Falkonry now offers a downloadable option for Private Deployments.

Falkonry AI Augments Operational Decision-making

As extreme growth in the generation of live operational data continues, the need to build solutions that recognize patterns in this data grows in parallel. Falkonry is committed to making AI-based pattern recognition an easy to incorporate component of any operations-oriented solution, and this release furthers us down that path.

For more information, please visit www.falkonry.com