Engineered for

Predictions at the Edge

EmcienPatterns is engineered to deliver continuous, high-accuracy predictions at the edge of the network, giving IoT devices the ability to sense future events and respond proactively in real time.

These key components combine to enable this edge analytics capability, all of which will be explored in further detail:
 

  1. A tiny footprint, which ensures compatibility with the smallest edge devices
  2. 2-tier architecture, which preserves continuous prediction in the absence of internet connectivity
  3. Speed and scale mechanisms and automation, which deliver timely predictions on high-speed edge data
  4. APIs, small model size, and C programming language, which simplify orchestration

A Tiny Footprint Ensures Compatibility with the Smallest Edge Devices

The small sizes of both Emcien’s predictive model and Edge Prediction module (EPM) combine to create a tiny footprint that’s compatible with the smallest IoT devices deployed at the edge of the network.

Predictive Model Requires Minimal Storage Space

Emcien’s Analysis module (small footprint software) analyzes historical data and produces a predictive model in the form of a concise set of predictive rules.
 
The rule set is a representation of only the outcome-driving relationships in the data, so it’s very small. The rule set is about half the size of an iTunes song, and approximately 0.01% of the original data size.
 
As a result of its small size, the model can easily be exported to, and fit on, small edge devices where storage space is very limited.

Small Edge Prediction Module Requires Minimal Hardware

In order to generate predictions, the predictive model must be applied to new, incoming data received at the edge of the network.
 
Because all of the predictive intelligence resides in the model itself, and the task of applying rules to data is relatively straightforward, the EPM itself can be a small program.
 
This small C program is compatible with most devices because it has very minimal hardware requirements:

  • 700 Mhz ARMv6 processor (equivalent to Raspberry Pi)
  • 512 MB RAM
  • 16 GB HD (e.g. SD Card)

The diagram below illustrates an example client implementation:

  • Multiple instances of the Edge Prediction module (EPM) have been spun up.
  • Each is installed on one of the client’s IoT devices at a remote client site.
  • The Analysis module has generated a small predictive model.
  • Multiple copies of the model have been made.
  • Each copy is sent to an EPM on an IoT device.
  • The EPMs apply their copies of the model to the new data collected by the IoT devices.
  • The EPMs generate predictions about critical events (i.e. equipment failure).
  • The devices use the predictions to respond in real-time, preventing predicted events from occurring.

2-Tier Architecture Preserves Continuous Prediction in the Absence of Internet Connectivity

Emcien’s 2-tier architecture ensures that both the Analysis module and Edge Prediction Module (EPM) are able to work independently of one another. As a result, the EPM can continue to generate predictions when internet connectivity is lost and modules cannot communicate with each other.

Emcien’s Analysis module (small footprint software) analyzes historical data and produces a predictive model in the form of a concise set of predictive rules.
 
The rule set is a representation of only the outcome-driving relationships in the data, so it’s very small. The rule set is about half the size of an iTunes song, and approximately 0.01% of the original data size.
 
As a result of its small size, the model can easily be exported to, and fit on, small edge devices where storage space is very limited.

Speed & Scale Mechanisms Plus Automation Deliver Timely Predictions on High-Speed Edge Data

Speed and scale mechanisms combine with automation capabilities to produce continuous, high-speed predictions that easily keep pace with fast-moving edge data. As a result, devices can respond quickly and prevent critical events from occurring.

Speed & Scale Mechanisms Ensure Predictions are High Speed

EmcienPatterns has been carefully designed to deliver enterprise-grade speed and scalability. You can learn more about the mechanisms that deliver this level of performance in our Speed & Scale technical document.

At 1,000 predictions per second on a single core, EmcienPatterns will never struggle to keep up with your high speed edge data.

Automation Means Predictions Never Stop

Just like the Analysis module, the Edge Prediction module can be automated so that it works continuously to apply the predictive model to new data.
 
Because it never stops to take a break and needs no human intervention, the EPM is never behind on prediction. It never amasses a backlog of old transactions or events or records to which the model must be applied in the future.
 
Instead, the moment new data is available, the EPM applies the predictive model and generates a prediction, ensuring there is never any insight lag.

APIs, Small Model Size & C Programming Language Simplify Orchestration

Our software’s APIs, the small size of our predictive model, and our use of the C programming language in our Edge Prediction module (EPM) combine to simplify our clients’ orchestration efforts.

APIs Make It Easy to Access the Predictive Model

Once the Analysis module updates the predictive model, this new rule set is needed by the Edge Prediction module (which must be installed by the client on their remote devices) so that it can generate predictions at the edge.

EmcienPatterns makes this rule set available through its RESTful APIs. As a result, the client can access it easily.

Model Is Small, Making Transport Easy

What’s more, because the model is extremely small, at about half the size of an iTunes song, it can be transported in a variety of ways.
 
No big file synchronization or other complex process is needed to transport the model. Rather, transport can be as simple as a web request.

C Programming Language Makes EPM Easy to Install

As previously described, the program that runs the Prediction Module is a simple C program. As a result, it’s quite small and will easily fit on a small remote device, making installation easy.
 
Additionally, because C is a universal, cross-platform language, it is compatible with virtually any edge device, further simplifying the installation process.

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