#Cloud

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®).

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)?

Hi Developers,

A new video is already on InterSystems Developers YouTube:

⏯ Creating an HL7 v2 to FHIR Transformation Pipeline with S3 Target

https://www.youtube.com/embed/3TKG1ECXfdc
[This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]

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.

https://www.youtube.com/embed/d2_1aptaL4E
[This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]

All source code to the article is available at: https://github.com/antonum/ha-iris-k8s

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).

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”).

Purpose

Most CloudFormation articles are Linux-based (no wonder), but there seems to be a demand for automation for Windows as well. Based on this original article by Anton, I implemented an example of deploying a mirror cluster to Windows servers using CloudFormation.I also added a simple walk through.
The complete source code can be found here.

Update: 2021 March 1 I added a way to connect to Windows shell by public key authentication via a bastion host as a one-liner.

Hello fellow community members,

I would like to offer my services as an Intersystems Professional and am available to work on projects.

I have more than a decade experience into Intersystems stack of technologies including IRIS, Ensemble, Healthshare, Healthconnect, Cache Objectscript, Mumps, Zen, Analytics etc. with companies spread over US and UK involved in multiple domains.

Hi Community,

See how to use System Alerting & Monitoring (SAM) to view and customize metrics for a cluster of systems running InterSystems IRIS:

⏯ Viewing & Customizing Metrics with SAM

Hi team.

Does anyone has any idea on how to send an encrypted / secure Print from IRIS ( which is hosted on AWS ) to a printer ( which is an on-premise device ).

Thanks

Paras

Hi Community!

Please welcome the new video on InterSystems Developers YouTube:

⏯ Get InterSystems IRIS from the Docker Store

https://www.youtube.com/embed/guTiFXELt8E
[This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]

Hi All,

With this article, I would like to show you how easily and dynamically System Alerting and Monitoring (or SAM for short) can be configured. The use case could be that of a fast and agile CI/CD provisioning pipeline where you want to run your unit-tests but also stress-tests and you would want to quickly be able to see if those tests are successful or how they are stressing the systems and your application (the InterSystems IRIS backend SAM API is extendable for your APM implementation).

Hi, someone have connected Iris to a Bucket ? like aws, huawei, etc.

I try to use private Bucket as storage for files, and Iris as database & API interface to manage this files, but al SDKs are in another languages, no one have founded on COS.

I have tested some examples, but with bad results