Derek Robinson · Jan 12
New Kubernetes Exercise!

Hi All! For those of you who attended experience labs at the 2021 Virtual Summit, you may recall that one of the lab sessions was around Kubernetes. We've now converted that lab to be fully on-demand. You can launch a small cluster of VMs and follow the exercise to manage your Kubernetes cluster, deploy InterSystems IRIS containers to it, and watch its self-healing nature when destroying a pod.

It's a great introduction to Kubernetes if you are interested! See here: Achieving High Availability with InterSystems IRIS and Kubernetes

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jennifer mccallum · Aug 4, 2021
Azure Mirroring - VIP

Has anyone firstly got VIP working for mirroring in Azure?  We are pretty sure that this won't work but I wanted to verify it.

Has anyone used the Azure Load balancer to act as a Virtual IP and if so was a application gateway required.

Interested to know your experiences in getting this working.

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We are ridiculously good at mastering data. The data is clean, multi-sourced, related and we only publish it with resulting levels of decay that guarantee the data is current. We chose the HL7 Reference Information Model (RIM) to land the data, and enable exchange of the data through Fast Healthcare Interoperability Resources (FHIR®).

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I wanted to write it as a comment to article of @Evgeny Shvarov . But it happens to be so long, so, decided to post it separately.

Image result for docker clean all images

I would like to add a bit of clarification about how docker uses disk space and how to clean it.  I use macOS, so, everything below, is mostly for macOS, but docker commands suit any platform.

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Oliver Wilms · Aug 4, 2021 3m read
IRIS Mirror in the cloud (AWS)

I have been working on redesigning a Health Connect production which runs on a mirrored instance of Healthshare 2019. We were told to take advantage of containers. We got to work on IRIS 2020.1 and split the database part from the Interoperability part. We had the IRIS mirror running on EC2 instances and used containers to run IRIS interoperability application. Eventually we decided to run the data tier in containers as well.

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Being equipped by science and technology, human being have walked a long way by great inventions such as steam-engines or aeroplannes; while after decades, people gradually recognize that single creation could not lauch an industry-boom again. That is why and when, technologies grow up with a community, where are we now=P. An eco-system of technology would be born with the power of a system and grow up with the power of system-science, such as InterSystems, with which seated the letters "s-y-s-t-e-m". Graduated with M.S.

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Hello Developers!

Have you ever had to convert HL7v2 messages to FHIR (Fast Healthcare Interoperability Resources) and found the process complicated and confusing? InterSystems is rolling out a new cloud based SaaS offering called HealthShare Message Transformation Services, which makes the process easy.  We are excited to announce an Early Access Preview Program for our new offering, and we would love to have you kick the tires and let us know what you think!  All you need is a free AWS account, with an S3 bucket to drop in your HL7v2 messages, and another S3 bucket to get your FHIR output. 

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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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Derek Robinson · Jun 16, 2021
New Video: Kubernetes Overview

If you're looking for an introduction to Kubernetes as a technology, and a little teaser about the InterSystems Kubernetes Operator, check out the recently released Kubernetes Overview video. This video will introduce you to the functionality and use cases of Kubernetes, and explain a bit about how the InterSystems Kubernetes Operator makes it beneficial to use Kubernetes with InterSystems IRIS.

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Robert Cemper · 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.

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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What is Distributed Artificial Intelligence (DAI)?

Attempts to find a “bullet-proof” definition have not produced result: it seems like the term is slightly “ahead of time”. Still, we can analyze semantically the term itself – deriving that distributed artificial intelligence is the same AI (see our effort to suggest an “applied” definition) though partitioned across several computers that are not clustered together (neither data-wise, nor via applications, not by providing access to particular computers in principle). I.e., ideally, distributed artificial intelligence should be arranged in such a way that none of the computers participating in that “distribution” have direct access to data nor applications of another computer: the only alternative becomes transmission of data samples and executable scripts via “transparent” messaging. Any deviations from that ideal should lead to an advent of “partially distributed artificial intelligence” – an example being distributed data with a central application server. Or its inverse. One way or the other, we obtain as a result a set of “federated” models (i.e., either models trained each on their own data sources, or each trained by their own algorithms, or “both at once”).

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