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#InterSystems IRIS for Health

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InterSystems IRIS for Health™ is the world’s first and only data platform engineered specifically for the rapid development of healthcare applications to manage the world’s most critical data. It includes powerful out-of-the-box features: transaction processing and analytics, an extensible healthcare data model, FHIR-based solution development, support for healthcare interoperability standards, and more. All enabling developers to realize value and build breakthrough applications, fast. Learn more.

Hi everyone,

I want to talk about our project and use the dataset theme for this contest.

Our intention never was to be a data curator, especially because sometimes my precious data means a lot for me, but not for the rest of the world.

My Precious

We want to go a step further and empower the user to find the perfect dataset for their needs.

Our project is a bridge between the data science community and the developer's community using InterSystems IRIS to achieve this mission.

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image

Hi Community,
In this article, I will demonstrate below steps to create your own chatbot by using spaCy (spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython):

  • Step1: Install required libraries

  • Step2: Create patterns and responses file

  • Step3: Train the Model

  • Step4: Create ChatBot Application based on the trained model

So Let us start.

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InterSystems FAQ rubric

You can use the %IndexBuilder class to perform index rebuilding using multiple processes.

Here is an example for the purpose of defining the standard index HomeStateIdx for the Home_State (state information of contact address) column of Sample.Person.

The steps are as follows:

1. Hide the index name to be added/rebuilt from the query optimizer.

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Continuing the description of work on the use of the openhl python module in productive mode.

Since the version of iris with Embedded Python, does not yet have a final release, it is already necessary to use it in production now. We decided to back up the service for exporting requests to a xlsx file on a separate server, and save the query result in a global in a separate database.

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Hi Community,

This article is a continuation of my article about Getting to know Python Flask Web Framework

In this article, we will cover the basics of topics listed below:

1. Routing in Flask Framework
2. Folder structure for a Flask app (Static and Template)
3. Getting and displaying data in the Flask application from IRIS.

So, let's begin.

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InterSystems FAQ rubric

You can use the system routine ^DBSIZE to estimate the backup file size (see also Note 1).

^DBSIZE estimates the file size of full, cumulative, and differential backups of the databases selected in the database backup list.

The database backup list is created from [System Administration] > [Configuration] > [Database Backup] > [Database Backup List] in the Management Portal.

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InterSystems FAQ rubric

You can set individual error pages for the following Web Gateway error messages/system responses:

  • server error
  • server busy
  • server unavailable
  • server timeout
  • connection closed

Settings are made on the Web Gateway Management screen ([Management Portal] > [System Administration] > [Configuration] > [Web Gateway Management] > [Configuration] > [Default Parameters]).

In the Error Page section of the Default Parameters menu, set the filename of the html page to display or the URL to redirect to when an error occurs.

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According to Databricks Apache Parquet is an open source, column-oriented data file format designed for efficient data storage and retrieval. It provides efficient data compression and encoding schemes with enhanced performance to handle complex data in bulk. Apache Parquet is designed to be a common interchange format for both batch and interactive workloads. It is similar to other columnar-storage file formats available in Hadoop, namely RCFile and ORC. (source: https://www.databricks.com/glossary/what-is-parquet).

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InterSystems FAQ rubric

Record maps are used to efficiently map files containing delimited records or fixed-width records to message classes used by the interoperability function, and to map files from interoperability function message classes to text files.

Record map mapping definitions can be created using the Management Portal, and we also provide a CSV record wizard that allows you to define while reading a CSV file.

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The InterSystems IRIS has a series of facilitators to capture, persist, interoperate, and generate analytical information from data in XML format. This article will demonstrate how to do the following:

  1. Capture XML (via a file in our example);
  2. Process the data captured in interoperability;
  3. Persist XML in persistent entities/tables;
  4. Create analytical views for the captured XML data.

Capture XML data

The InterSystems IRIS has many built-in adapters to capture data, including the next ones:

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Article
· Apr 20, 2021 3m read
Why gj :: locate?

You may think it isn’t too difficult to get from label+offset^routine to the actual source line responsible for the error. For an expert it isn't that hard... most of the time. But there are enough oddities and special rules that even an expert can get it wrong, whilst spending a lot of time trying to get there.

gj :: locate is the latest tool from George James Software – it debugs any error, class or routine by converting the location of an error in compiled .int code to the corresponding location in your source, and then taking you right there.

Image this scenario…

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Being interoperable is more and more important nowadays. InterSystems IRIS 2022.1 comes with a new messaging API to communicate with event streaming platforms like Kafka, AWS SQS/SNS, JMS and RabbitMQ.

This article shows how you can connect to Kafka and AWS SQS easily.
We start with a brief discussion of the basic concepts and terms of event streaming platforms.

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InterSystems FAQ rubric

※Use this method if you want to compare databases that have been replicated using mirroring, shadowing, or some other mechanism.

You can use the DATACHECK utility to compare global variables. Please refer to the document below.
Overview of DataCheck [IRIS]

***

Routine comparisons use the system routine %RCMP or the Management Portal.

Below is how to use it in the Management Portal.

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Index

This is a list of all the posts in the Data Platforms’ capacity planning and performance series in order. Also a general list of my other posts. I will update as new posts in the series are added.

You will notice that I wrote some posts before IRIS was released and refer to Caché. I will revisit the posts over time, but in the meantime, Generally, the advice for configuration is the same for Caché and IRIS. Some command names may have changed; the most obvious example is that anywhere you see the ^pButtons command, you can replace it with ^SystemPerformance.

While some posts are updated to preserve links, others will be marked as strikethrough to indicate that the post is legacy. Generally, I will say, "See: some other post" if it is appropriate.

Capacity Planning and Performance Series

Generally, posts build on previous ones, but you can also just dive into subjects that look interesting.

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In this article we are going to see how we can use the WhatsApp instant messaging service from InterSystems IRIS to send messages to different recipients. To do this we must create and configure an account in Meta and configure a Business Operation to send the messages we want.

Let's look at each of these steps in more detail.

Setting up an account on Meta

This is possibly the most complicated point of the entire configuration, since we will have to configure a series of accounts until we can have the messaging functionality.

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¡Hi everybody!

As you likely are aware, the new version of InterSystems IRIS for Health (I4H) it's already available in Docker Hub. It's the Community version and is free and fully functional. There have been comments about it in other articles and posts,... so today I won't add anything about features. Here I want to explore "the mistery about the disappearance, or better, absence of our persistent data when we run a container with the durable option" (I didn't find a terrifying font to emphasize the thriller... post editor is not terrific for styling smiley ) .

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A More Industrial-Looking Global Storage Scheme

In the first article in this series, we looked at the entity–attribute–value (EAV) model in relational databases, and took a look at the pros and cons of storing those entities, attributes and values in tables. We learned that, despite the benefits of this approach in terms of flexibility, there are some real disadvantages, in particular a basic mismatch between the logical structure of the data and its physical storage, which causes various difficulties.

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My team is working on redesigning an Interoperability solution that currently runs on a HealthShare server that is part of a mirror. Most of the messages are delimited records processed using Complex Record Mapping. We were told to utilize cloud services available in AWS, use containers, autoscaling…

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