Previously I have already tried to play with Google Data Studio when I connected it to InterSystems FHIRaaS. It has quite a nice UI, with a few chart types available out of the box, it can be quite easily connected to some plain tables (stored as CSV or JSON, for instance), and gives the ability to build quite flexible analytics over it. So, I have decided to implement a new connector to InterSystems Analytics (DeepSee), with the ability to select a cube and do some queries on it.

The OKR methodology (Objectives and Key Results or Objectives and Key Results) is used by the largest companies in the world (such as Google, Netflix, Spotify, BMW, Linkedin, etc.) for agile performance management. It was created in the 1970s by Andrew Grove, president of Intel, and introduced to the general public in his famous book “High Output Management”.

Around 1998 John Doerr, one of the world's top venture capitalists, after coming into contact with Intel's OKR, introduced the model to Larry Page and Sergey Brin, who started a small company called Google.

1. Integrated ML Demonstration

This repository is a demonstration of IntegratedML and Embedded Python.

• 1. Integrated ML Demonstration
• 2. Building the demo
  • 2.1. Architecture
  • 2.2. Building the nginx container
• 3. Running the demo
• 4. Python back-end
  • 4.1. Embedded Python
  • 4.1.1. Setting up the container
  • 4.1.2. Using Embedded Python
  • 4.1.3. Side By Side Comparaison
  • 4.2. Launching the server
• 5. IntegratedML
  • 5.1. Exploring both datasets
  • 5.2. Managing models
  • 5.2.1. Creating a model
  • 5.2.2. Training a model
  • 5.2.3. Validating a model
  • 5.2.4. Making predictions
• 6. Using COS
• 7. More explainability with DataRobot
• 8. Conclusion

The first installment of this article series discussed how to read a big chunk of data from the raw body of an HTTP POST method and save it to a database as a stream property of a class. The second installment discussed how to send files and their names wrapped in a JSON format.

Now let’s look closer at the idea of sending large files in parts to the server. There are several approaches we can use to do this. This article discusses using the Transfer-Encoding header to indicate chunked transfer. The HTTP/1.1 specification introduced the Transfer-Encoding header, and the RFC 7230 section 4.1 described it, but it’s absent from the HTTP/2 specification.

Hi developers!

How to build an analytics solution with InterSystems IRIS?

To begin with, let's agree on the points of what is the analytics solution - and this could be a very wide topic. Let's limit the set of solutions to those you can present in the Analytics contest.

There are three kinds of analytics solutions that we will review here: monitoring, interactive analytics, and reporting.

Monitoring

The typical monitoring solution consists of an online dashboard with KPIs that are being actively updated.

The key use case is of monitoring is to visually observe the KPI of fresh data every moment to react in case of an emergency.

Interactive Analytics

This solution supposes a set of interactive dashboards with filters and drill-downs.

The key use case is to explore the data with filters and drill-downs making business decisions upon graph and table data visualization.

Reporting

Reporting solution provides a set of static (usually) reports in a form of HTML or pdf documents that deliver the data in graph and text form in a predesigned form and could be sent via email.

The typical use case of a reporting system is to obtain reports on a given period that will illustrate the status of the product, process, service, sales, etc that is crucial for the business.

How InterSystems products could be used to build such solutions? Let's discuss this below!

Hi Community,

The InterSystems Learning Website has many important iterative courses. So if you want to learn about InterSystems and start to work with InterSystems this is the path:

Hi developers!

Just want to share an old but always relevant best practice on namespaces changing @Dmitry Maslennikov shared with me (again).

Consider method:

classmethod DoSomethingInSYS() as %Status

{

set sc=$$$OK

set ns=$namespace

zn "%SYS"

// try-catch in case there will be an error

try {

// do something, e.g. config change

}

catch {}

 zn ns    ; returning back to the namespace we came in the routine

return sc

}

And with new $namespace the method could be rewritten as:

classmethod DoSomethingInSYS() as %Status

{

set sc=$$$OK

new $namespace

set $namespace="%SYS"

// do something

return sc

}

So! The difference is that we don't need to change the namespace manually as it will be back automatically once we return the method.

and we don't need try-catch (at least for this purpose) too.

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.

Easy, easy, I'm not promoting a war against the machines in the best sci-fi way to avoid world domination of Ultron or Skynet.
Not yet, not yet 🤔

I invite you to challenge the machines through the creation of a very simple game using ObjectScript with embedded Python.

Hi community,

I'm going to publish several articles on how to perform actions that you can do in the web portal but via code.

Today.... Web Applications via code

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)?

If you develop in IRIS you are confronted with two major phenomena:

• an incredibly fast and excellent designed data storage engine
• a scripting language to work on this storage engine, actually named ObjectScript