Article
· Jan 19 4m read
Querying Dynamic Data using ASQ

IRIS 2023.3.0 ASQ

Abstract Set Query, ASQ, is a super set of JSON Path Language, JPL, which was first introduced by the 2016 ISO SQL Standard. JPL is only defined within the context of SQL. In 2023.3, we introduced classes that implement ASQ. It works on any implementation of %Library.AbstractSet. The complete syntax for ASQ will be documented later. This is just a quick introduction to a few of its features. This implementation comes with a nice utility as well. You will know it when you see it!

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Here in %SYS, we have already examined users, resources, and roles. Now that we know how to set all of that up, we should give it a purpose. Next we will talk about applications! As you may expect, we will see various identical class methods defined here that we have seen in the previous classes. However, some of them will have some tiny yet significant differences.

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With the advent of Embedded Python, a myriad of use cases are now possible from within IRIS directly using Python libraries for more complex operations. One such operation is the use of natural language processing tools such as textual similarity comparison.

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The %CSP.Login class is the utility class provided by InterSystems IRIS to do custom login pages. If you want to control your IRIS application authentication UI, you must extend %CSP.Login and override some methods according to your needs. This article is going to detail those methods and what you can do with them. In addition to that, you will get an explanation of the delegated authentication mechanism provided by ZAUTHENTICATE.mac routine.

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Artificial intelligence is not limited only to generating images through text with instructions or creating narratives with simple directions.

You can also make variations of a picture or include a special background to an already existing one.

Additionally, you can obtain the transcription of audio regardless of its language and the speed of the speaker.

So, let's analyze how the file management works.

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A customer recently asked if IRIS supported OpenTelemetry as they where seeking to measure the time that IRIS implemented SOAP Services take to complete. The customer already has several other technologies that support OpenTelemetry for process tracing. At this time, InterSystems IRIS (IRIS) do not natively support OpenTelemetry.

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We return with our example of using the FHIR Adapter, in this article we are going to review how we can configure it in our IRIS instances and what the result of the installation is.

The steps taken to configure the project are the same as indicated in the official documentation, you can review them directly here. Well, let's get to work!

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I recently had the need to monitor from HealthConnect the records present in a NoSQL database in the Cloud, more specifically Cloud Firestore, deployed in Firebase. With a quick glance I could see how easy it would be to create an ad-hoc Adapter to make the connection taking advantage of the capabilities of Embedded Python, so I got to work.

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InterSystems FAQ rubric

ObjectScript allows you to pass any number of arguments using arrays. Do it by adding ... after the argument name.

An example is as follows. In the example statement, the argument information is set in a global variable (a variable stored in the database) so that it can be easily checked after the method is executed.

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InterSystems FAQ rubric

To resolve the error <PROTECT>, remove the read-only attribute of the system-wide library database (IRISLIB for InterSystems IRIS, CACHELIB for Caché/Ensemble/HealthShare (Caché-based))

Once you have finished importing the routine, remember to change it back to read-only.

[Version 2013.1 and above]
[Management Portal] > [System Administration] > [Configuration] > [System Configuration] > [Local Database] Uncheck "Mount read-only" from the database name link.

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Article
· Feb 24 2m read
The bewitched line terminator

I want to address the nasty problems about reading a flat text in ASCII, UTF*
explicitly excluding HTML, EBCDIC, and other encoding.
According to Wikipedia there are at least 8 variations of control characters.

  • CR+LF is typical for Windows
  • LF is typical for the Linux/UNIX world
  • CR is Mac's favorite

As you can deduct from the names the inspiration comes from mechanical typewriters.

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We all know that having a set of proper test data before deploying an application to production is crucial for ensuring its reliability and performance. It allows to simulate real-world scenarios and identify potential issues or bugs before they impact end-users. Moreover, testing with representative data sets allows to optimize performance, identify bottlenecks, and fine-tune algorithms or processes as needed. Ultimately, having a comprehensive set of test data helps to deliver a higher quality product, reducing the likelihood of post-production issues and enhancing the overall user experience.

In this article, let's look at how one can use generative AI, namely Gemini by Google, to generate (hopefully) meaningful data for the properties of multiple objects. To do this, I will use the RESTful service to generate data in a JSON format and then use the received data to create objects.

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When analyzing data, there is often a need to look at specific indicators more thoroughly and to highlight sections of information of particular interest to a user.

For instance, examining the data dynamics for specific regions or dates can help us uncover some hidden trends and patterns that will allow us to make an informed decision about our project in the future.

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When creating custom Business Hosts, it's often necessary to add properties to the class for additional settings that will be used in the initialization or operation of the host. The property name itself isn't always very descriptive, so it's an advantage to have a custom caption display with the field.

In Caché, it was fairly straightforward:

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In part I of this article, we have already learned more about HL7, its messages, structure, segments, and fields. In this part II, we will show you how to transform proprietary/custom data into HL7 using IRIS for Health. For our sample, we used the data generated by the SYNTHEA bulk data generation project (https://synthea.mitre.org/downloads). So, we will show you how to convert 1000 patients from a CSV file to HL7 v2, using the Interoperability features of IRIS for Health.

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You may have already heard that, starting with IRIS and HealthShare HealthConnect 2023.2 versions, the internal Apache Server will be removed from the default installation, so it will be necessary to have an external application server such as Apache Server or NGINX.


In this article I am going to proceed to install a HealthShare HealthConnect 2023.1 so that it works with a pre-installed Apache Server. For this I will use a virtual machine on which I have installed an Ubuntu 22.04.

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