InterSystems technologies are renowned for their high performing databases, which support the systems and operations of many organisations. However a key ingredient to this success is the quality and maintainability of their code.

The quality of code can impact everything from speed and ease of fixing bugs and making enhancements, to the overall performance of your organization and your ability to get ahead in the marketplace.

By ensuring your code is maintainable, you can reduce approximately 75% of the systems life cycle costs*. This is why, at George James Software, the solutions we build are always straightforward and written in high quality code - because we know that this solid foundation can positively impact the rest of your organization.

With a maintainable system you're able to reduce your overall maintenance as any issues that occur are significantly faster to identify and fix. This means you're free to allocate that time and budget to enhancements, enabling you to get the most value out of your applications and ultimately better support your organization.

Keep an eye out for our next few posts about what a maintainable system looks like and the tools that can help you keep your code maintainable, in order to help you to reduce those maintenance costs.

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Article
· Nov 23, 2021 4m read
Mutual TLS setup

Hi,

I recently needed to setup an SSL/TLS configuration in IRIS that supported mutual authentication (where the server IRIS is establish a connection to is verified, and, where IRIS is in turn verified by the remote host). After a bit of research and getting it done, I thought it worthwhile to just go over the process I went through in order to potential help others, and save you some time .

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The ObjectScript language of InterSystems IRIS has a very powerful metadata engine called XData. This feature allows the creation of metadata definitions for your classes, to be used by the compiler or by programs that will extend the standard features of the language, based on the XData definitions of its scope.

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The OKR methodology (Objectives and Key Results or Objectives and Key Results) is used by the largest companies in the world (such as Google, Netflix, Spotify, BMW, Linkedin, etc.) for agile performance management. It was created in the 1970s by Andrew Grove, president of Intel, and introduced to the general public in his famous book “High Output Management”.

Around 1998 John Doerr, one of the world's top venture capitalists, after coming into contact with Intel's OKR, introduced the model to Larry Page and Sergey Brin, who started a small company called Google.

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I wrote a step by step tutorial in the qewd-howtos repository how you can write state of the art multi-page web apps with Node.js using a QEWD-Up WebSocket/REST api back-end integrated with a mainstream web framework like NuxtJS & Vue.js.

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Hello, developers!

I would like to share the history of the project - the ZAPM shell.

As soon as ZPM was implemented, I immediately began to think about how best to use it.

And immediately faced with the desire to move more quickly between namespaces, especially when there are more than 20 of them.
I had to leave the ZPM, move to the desired namespace and re-enter the shell.
I suggested an improvement - a new "namespace" command for easier navigation.
I didn’t wait - I did it myself. And so it went. If you need it, get ready to do it yourself.

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Article
· Feb 8, 2021 2m read
Websocket Client Embedded Python

This is a demo to make use of a simple WebSocket Client with Embedded Python in IRIS.

How to Test it

  • Run an Iris Session in Docker
  • Select your WebSocket Echo Server
  • Enter the text you want to send or generate it
  • Send it and see the result
$ docker-compose exec iris iris session iris "##class(rccpy.WSockPy).Run()"

*** Welcome to WebSocket Embedded Python Demo ***

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Article
· Dec 25, 2021 2m read
AOC2021-rcc

After >40 years of writing in-countable lines of code in M*/COS/ISOS (and a bunch of archaic languages)
I decided for myself to set a strong signal for the future. We have Embedded Python available
(still pre-release)! I just felt it as a sacrilege to ignore this excellent NEW opportunity and
stay with the old sermon that I had used for decades.

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Hey everyone!

I recently learnt something new while working with WRC on an issue, and I wanted to share with everyone on the off chance it could help someone else.

Scenario:

Files are being inexplicably written to a folder on your server and, due to the number of files in the folder and general system throughput, it is not possible to work through the files to track down the source.

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Article
· Jun 5, 2021 8m read
FHIRaaS overview

Introduction

This article aims to provide an overview of InterSystems IRIS FHIR Accelerator Service (FHIRaaS) driven by the implementation of application iris-on-fhir, available in OEX developed for the FHIRaaS contest.

A basic tutorial will guide you in configuring a function FHIRaaS deployment, including an API key and an OAuth 2.0 server.

A library to use FHIR resources through FHIRaaS also is briefly discussed.

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Fixing the terminology

A robot is not expected to be either huge or humanoid, or even material (in disagreement with Wikipedia, although the latter softens the initial definition in one paragraph and admits virtual form of a robot). A robot is an automate, from an algorithmic viewpoint, an automate for autonomous (algorithmic) execution of concrete tasks. A light detector that triggers street lights at night is a robot. An email software separating e-mails into “external” and “internal” is also a robot. Artificial intelligence (in an applied and narrow sense, Wikipedia interpreting it differently again) is algorithms for extracting dependencies from data. It will not execute any tasks on its own, for that one would need to implement it as concrete analytic processes (input data, plus models, plus output data, plus process control). The analytic process acting as an “artificial intelligence carrier” can be launched by a human or by a robot. It can be stopped by either of the two as well. And managed by any of them too.

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Previously I have already tried to play with Google Data Studio when I connected it to InterSystems FHIRaaS. It has quite a nice UI, with a few chart types available out of the box, it can be quite easily connected to some plain tables (stored as CSV or JSON, for instance), and gives the ability to build quite flexible analytics over it. So, I have decided to implement a new connector to InterSystems Analytics (DeepSee), with the ability to select a cube and do some queries on it.

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The most recent release of Serenji features our innovative gj::locate technology. It was a standalone tool we originally created for a Developer Community contest earlier this year, but we've incorporated it into our debugger after some great feedback from developers. 

It works by navigating you directly to the source of your server-side errors in just a couple of clicks - enabling you to quickly fix errors without the need to count tedious lines of code... and let's be real, who has got time for that when you're under pressure to fix this bug? 

It's simple and straightforward to use:

1. Click on the gj::locate panel in the status bar

2. Enter the ObjectScript error message or line reference from a class/.mac routine..

3. gj::locate then does the work for you by taking you straight to the corresponding line in your source code.

Easy peasy... and with time to spare to make yourself a coffee before your deadline! 

The video below shows it in action - let us know if you've already given it a go. Or, if you're interested in trying it out we offer a free 30 day trial license, just drop me a message through the Developer Community or email us at info@georgejames.com.

Serenji 3.2.0 utilising gj::locate technology

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install and usage

Packed Pretty.xml installs routine ZPretty in any namespace.
calling $$Do^ZPretty(input,[filler],[newline]) returns a wrapped JSON string.
filler is the optional string for the indent, default = " "
newline is optional, default = $C(13,10)
input accepts: JSON_String, JSON_Stream, %DynamicAbstractObject

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Introduction

In the first article, a simple tutorial helped you to set up your FHIRaaS deployment.

Now, let's move forward and introduce a JS library to access the FHIR resource.

In the end, two examples of usage of this library will be presented, exploring the Appointment FHIR resource type.

SMART on FHIR JavaScript Library

FHIR is a REST API, so you can use any HTTP client in order to use it. But, it’s always a good idea to have help.

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Challenges of real-time AI/ML computations

We will start from the examples that we faced as Data Science practice at InterSystems:

  • A “high-load” customer portal is integrated with an online recommendation system. The plan is to reconfigure promo campaigns at the level of the entire retail network (we will assume that instead of a “flat” promo campaign master there will be used a “segment-tactic” matrix). What will happen to the recommender mechanisms? What will happen to data feeds and updates into the recommender mechanisms (the volume of input data having increased 25000 times)? What will happen to recommendation rule generation setup (the need to reduce 1000 times the recommendation rule filtering threshold due to a thousandfold increase of the volume and “assortment” of the rules generated)?
  • An equipment health monitoring system uses “manual” data sample feeds. Now it is connected to a SCADA system that transmits thousands of process parameter readings each second. What will happen to the monitoring system (will it be able to handle equipment health monitoring on a second-by-second basis)? What will happen once the input data receives a new bloc of several hundreds of columns with data sensor readings recently implemented in the SCADA system (will it be necessary, and for how long, to shut down the monitoring system to integrate the new sensor data in the analysis)?
  • A complex of AI/ML mechanisms (recommendation, monitoring, forecasting) depend on each other’s results. How many man-hours will it take every month to adapt those AI/ML mechanisms’ functioning to changes in the input data? What is the overall “delay” in supporting business decision making by the AI/ML mechanisms (the refresh frequency of supporting information against the feed frequency of new input data)?

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