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This is a coding example working on IRIS 2020.2 
It will not be kept in synch with new versions 
It is also NOT serviced by InterSystems Support !

My demo video is now also available to watch the demo in operation.
https://youtu.be/dSV-0RJ5Olg

 

Hi folks,
It's time now for a Micro Service Demo with a total fresh IRIS Image and an image that you both PULL with
docker  and run with only 4 lines of docker commands. 
June 1st, 2020 - rcc

There is now a compact All-in-1 version available that combines all parts in a single container image.
For details see: IRIS-NativeAPI-Nodejs-compact
May 24, 2020 - rcc

I have added a simplified installation using Docker, see context
May 25, 2020 - rcc

There are enhanced scripts suitable & tested for Linux & Windows available here
https://github.com/rcemper/WSockClientMicroSV/blob/master/READMEwindows.MD
May 26, 2020 - rcc

This demo is a redesign of the WebSocket Client based on Node.js existing already for Caché. The major changes:

  • use of the new IRIS Native API for Node.js  especially Working with Global Arrays
  • change from a directly triggered client to a server design
  • put the result into a separate docker image as an example for a MicroService / MicroServer
  • add a simple interface in IRIS to control the MicroService execution.

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Let's imagine if you would like to write some real web application, for instance, some simple clone of medium.com. Such sort of application can be written using any different language on the backend side, or with any framework on the frontend side. So many ways to do the same application, and you can look at this project. Which offers a bunch of frontends and backends realizations for exactly the same application. And you can easily mix them, any chosen frontend should work with any backend.

Let me introduce the same application realization for InterSystems IRIS on a backend side.

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Currently, the process of using machine learning is difficult and requires excessive consumption of data scientist services. AutoML technology was created to assist organizations in reducing this complexity and the dependence on specialized ML personnel.

AutoML allows the user to point to a data set, select the subject of interest (feature) and set the variables that affect the subject (labels). From there, the user informs the model name and then creates his predictive or data classification model based on machine learning.

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In the past, reading information from a bar code was limited to a simple alphanumeric code. The creation of a bar code with more than one dimension (2D), especially the QR Code, allowed to increase the amount and variety of data stored in a bar code. While conventional bar codes are capable of storing a maximum of approximately 20 digits, the QR Code is capable of handling several tens to hundreds of times more information. This revolutionized the markets. Now QR codes are everywhere and can be very useful for storing textual, numeric, alphanumeric and even binary data.

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Article
Robert Cemper · Jul 20, 2020 1m read
IRIS-NativeAPI-Nodejs-compact

This is a follow-up to my previous article WebSocket Client JS with IRIS Native API as Docker Micro Server

Installation is now much simpler as all pieces are now assembled in a single Docker image.
That makes life easier. But of course, the principle of the Micro Service is not so obvious anymore.
An All-in-1 bundled package. Therefore compact.

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Article
Oliver Wilms · Oct 5, 2020 2m read
File Passthrough Feeder

IRIS Interoperability Productions formerly known as Ensemble are fun to work with. Yes, I really think my work is fun. I have seen File Passthrough Services and File Passthrough Operations come in handy. At one point we placed test messages in files, then we utilized a File Passthrough Service with Inbound File Adapter to send the contents of the file as a Stream to a File Passthrough Operation with Outbound TCP Adapter.

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A few months ago, I read this interesting article from MIT Technology Review, explaing how COVID-19 pandemic are issuing challenges to IT teams worldwide regarding their machine learning (ML) systems.

Such article inspire me to think about how to deal with performance issues after a ML model was deployed.

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These Competition Terms ("Terms") apply to competitions and contests sponsored by Intesystems and its affiliates, including coding contests relating to InterSystems Products and technologies (each referred to as a "Contest"). Please read these Terms and all applicable Rules (referenced below) carefully as they form a binding legal agreement between you and InterSystems Corporation, with principal office located at1 Memorial Drive Cambridge, MA, 02142 UNITED STATES ("InterSystems") governing the rules and rewards of the Contests.

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