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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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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Article
· Jun 12, 2021 2m read
Running reviews on Open Exchange

For several weeks I'm creating reviews on OEX.
So I'd like to explain to you the criteria that I apply to find my ratings.
Of course, each reviewer is an independent person and has his own criteria and his own opinion.
And that's good and important! As Winston Churchill once said:

"If 2 people always have the same opinion, then 1 of them is superfluous"

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Google has one intersting tool named Data Studio. This tool allows creating some interactive dashboards, based on your data, available from the internet. It already offers hundreds of connectors to any sort of data developed by the community. As well as some amount of community developed visualizing. And most importantly, Google offers a way to develop your own connector to your data.

FHIRaaS provides a REST API, and it's available from the internet. So I've decided to try to create some basic report on data stored there. And in the end, I got this.

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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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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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IMPORTANT NOTE InterSystems no longer provides a separate InterSystems Reports Server container. To run containerized InterSystems Reports Server, use Logi Reports Server container and your InterSystems Reports Server license. Documentation.

InterSystems Reports is powered by Logi Report (formerly named JReport), a product of Logi Analytics. InterSystems Reports is supported by InterSystems IRIS and InterSystems IRIS for Health. It provides a robust modern reporting solution that includes:

  • Embedded operational reporting which can be customized by both report developers and end users.
  • Pixel-perfect formatting that lets you develop highly specific form grids or other special layout elements for invoices, documents, and forms.
  • Banded layouts that provide structure for aggregated and detailed data.
  • Exact positioning of headers, footers, aggregations, detailed data, images, and sub-reports.
  • A variety of page report types.
  • Large-scale dynamic report scheduling and distribution including export to PDF, XLS, HTML, XML, and other file formats, printing, and archiving for regulatory compliance.

InterSystems Reports consists of:

  • A report designer, which provides Design and Preview Tabs that enable report developers to create and preview reports with live data.
  • A report server which provides end users browser-based access to run, schedule, filter, and modify reports.

From InterSystems documentation.

This article focuses on the Server part of InterSystems Reports and provides a guide on running Report Server in containers while persisting all the data.

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InterSystems SAM is a great tool to monitor your InterSystems IRIS and InterSystems IRIS For Health clusters on prem or in a cloud environment. This article describes how you can implement a customized alert handler. This is currently an undocumented and most likely an unknown feature of InterSystems SAM. With future releases it will be probably made easier to leverage this useful concept.

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Article
· May 28, 2021 1m read
Fetch Upstream in GitHub

Hi colleagues!

Often when we collaborate to someone's repo in GitHub we do the following cycle:

Fork-Clone-Change-Commit-Push-Pull-Request-Merge to the original repo.

This is all great and works fine!

And if we want to make a second collaboration right after the merge you need to perform "Fetch upstream" to your forked repo first to "ingest" your own Pull-request in the original repo.

Geeky git-professionals do it with ease but this was always a headache for me so I usually simply deleted the fork and created a new one.

And today I figured that Github added a new UI feature that I can easily fetch-upstream for my fork with the original one and make it up to date and capable for pull-requests.

Here is where the button is:

This is a relief! )

Wanted to share this relief and productivity tip with you!

Bring more collaborations to Github repos!

And speaking of PR - I just made a PR with docker to Google Cloud Run deployment for the FHIRaaS demo made by @Anton Umnikov for the current FHIR Contest! Looking for more of your contributions!

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Article
· May 24, 2021 1m read
Data Platform Levels

It's a challenge when you need, as a software architect, design a corporate architecture to meet the current business requirements, you need achieve level 5. With InterSystems IRIS.
it's possible. With 1 product you get SQL + NoSQL + ESB + BI + Open Analytics + Real time virtual cubes + NLP + AutoML + ML (with Python) and Advanced cloud + Sharding support.

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At the heart of IRIS and Cache is a very interesting database architecture that we, at M/Gateway Developments, refer to as "Global Storage". If you ever wanted to know more about the fundamentals and capabilities of this underlying database, you might want to read a major analysis we've put together:

https://github.com/robtweed/global_storage

Amongst other things you'll discover that:

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Article
· May 11, 2021 8m read
IRIS in Astronomy

In this article we are going to show the results of the comparision between IRIS and Postgress when handling Astronomy data.

Introduction

Since the earliest days of human civilization we have been fascinated by the sky at night. There are so many stars! Everybody has dreamed about them and fantasized about life in other planets.

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When you have been using cubes for business intelligence in a namespace for some time, you may find that there are many cubes in the namespace, only some of which are actively being used. However, it can be difficult to tell which cubes users are or are not querying, and maintaining unused cubes can be costly both in terms of storage and of computation to keep them up to date. This article provides some suggestions and examples for monitoring which cubes are in active use, and for removing cubes that you determine are no longer necessary.

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Article
· Apr 26, 2021 3m read
SSH for IRIS container

Why SSH ?

If you do not have direct access to the server that runs your IRIS Docker container
you still may require access to the container outside "iris session" or "WebTerminal".
With an SSH terminal (PuTTY, KiTTY,.. ) you get access inside Docker, and then, depending
on your needs you run "iris session iris" or display/manipulate files directly.

Note:
This is not meant to be the default access for the average application user
but the emergency backdoor for System Management, Support, and Development.

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Like hardware hosts, virtual hosts in public and private clouds can develop resource bottlenecks as workloads increase. If you are using and managing InterSystems IRIS instances deployed in public or private clouds, you may have encountered a situation in which addressing performance or other issues requires increasing the capacity of an instance's host (that is, vertically scaling).

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