Hi community,

This is another article about how to perform actions that you can do in the web portal but via code.

Today.... Add a default setting value by code

This article is a follow-up to this post.

In the previous article, [What is a Production?], we checked the production contents. We ran the sample code and checked the flowing messages' contents into the production on the Visual Trace page.

Accuracy rate is a measure of our prediction results, and it is a measure of how many of the predicted positive samples are actually positive.
The recall rate is for our original sample, and it shows how many positive examples in the sample were correctly predicted.
Accuracy = Predicted correctly/All
If we want to be able to retrieve as much content as possible, this is the pursuit of "recall ratio", namely A/(A+C), the bigger the better.

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.

Hi developers!

ZPM Package Manager is getting more and more popular and thus the library of packages is growing.

And thus some solutions or samples can be built with InterSystems IRIS and one or two packages installed.

I want to share with you iris-docker-zpm-usage-template - a template that can be used exactly for this purpose.

This is a first attempt to use Embedded Python in IRIS
The Python code is adapted from solutions for Advent of Code 2020 contest.
Test data are all input to my personal challenge.

Prerequisites

Make sure you have git and Docker desktop installed.

Installation

Clone/git pull this repo into any local directory

Let me introduce the support of InterSystems IRIS in IntelliJ IDEA. This plugin adds Syntax Highlighting support for ObjectScript, and auto import and compile on the server after saving a changed file. It uses LanguageServer written in Rust, where was added an ability to import and compile code.

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.

One of our apps uses a class query to support a ZEN Report and works just fine in that report, producing the expected results every time. We’ve since migrated to InterSystems Reports and noticed that, for a report using the same class query, 100s of extra rows with the same column values appear at its bottom.

This is the third article in our short series around innovations in IRIS SQL that deliver a more adaptive, high-performance experience for analysts and applications querying relational data on IRIS. It may be the last article in this series for 2021.2, but we have several more enhancements lined up in this area. In this article, we'll dig a little deeper into additional table statistics we're starting to gather in this release: Histograms

Image search like Google's is a nice feature that wonder me - as almost anything related to image processing.

A few months ago, InterSystems released a preview for Python Embedded. As Python has a lot of libs for deal with image processing, I decided to start my own attemptive to play with a sort of image search - a much more modest version in deed :-)

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)?

In this article, we’ll look at one of the ways to monitor the InterSystems IRIS data platform (IRIS) deployed in the Google Kubernetes Engine (GKE). The GKE integrates easily with Cloud Monitoring, simplifying our task. As a bonus, the article shows how to display metrics from Cloud Monitoring in Grafana.

Note that the Google Cloud Platform used in this article is not free (price list), but you can leverage a free tier. This article assumes that you already have a project in the Google Cloud Platform (referred to as <your_project_id>) and have permission to use it.

Developing the project zpm "install isc-apptools-lockdown" implemented the ability to set the security level not only to lockdown, but also to minimum and normal.

Increasing security settings

You can replace the shared password if the password of the predefined system users has been compromised