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Announcement
· Jun 11, 2020

Visualising your Global Storage using QEWD Monitor

The latest WebComponent-based, SB Admin 2-themed QEWD Monitor application now includes a cool D3-based viewer for visualising your IRIS or Cache Globals - see example below

This qewd-monitor-adminui application is automatically installed when you install QEWD on a Windows machine

See here for details on installing QEWD on Windows:

https://github.com/robtweed/qewd-microservices-examples/blob/master/WINDOWS-IRIS-2.md#initial-steps
 

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Article
· Jun 10, 2020 3m read

Mapping Intersystems IRIS documentation to a Corporate Data Architecture

If you need write your organization Data Architecture and map to the InterSystems IRIS, consider following Data Architecture Diagram and references to the intersystems iris documentation, see:

 

Architecture mapping:

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Article
· Jun 5, 2020 3m read

Help My ChatBots to Learn the language!

Hi everyone! If it doesn't bother you, could you help me teach my bots to talk?

Open my chatbot here: Help my chatbots to talk!

What? Aren't your chatbots smarts?

Smart isn't the best term for this scenario. They are trained but with little data! Most of chatbot solutions uses Machine Learning to create a way to talk with people and Machine Learning needs one important thing to performs well: DATA

How does it work?

A simple way to explain: imagine someone who has a brain but dont have any experience in his life he just born. In this scenario the person has to learn how to speak looking others speaking taking classes watching movies etc. This human learning process can be comparable to a machine learning model. You have to expose the machine learning model to situations that can teach him and these situation could be DATA.

 So a chatbot its just a dictionary or a parrot...

Absolutely not. The first problem to use this approach is: for each sentence that the chatbot receives someone has to teach exactly what to answer. We can go inside this problem and try to predict how many different sentences we can form... but obviously is not a way to solve the problem. To solve this problem there are plenty machine learning techniques to after training the model with certain data amount it will perform well in subjects next to the training data. For it the program use data of other conversations to learn patterns, words, synonyms, meanings and this process result in a machine learning model.

What exactly your chatbots are capable to do?

Now they just can predict the sentence intention and with this prediction answer you. Imagine after training the bot he just have those intentions in "mind":

  • Intention: "Greeting" , Answer: "Hi!"
  • Intention: "Goodbye" , Answer: "Bye!"
  • Intention: "Stop speaking." , Answer: "ok."

And a person send a sentence to him: "Did you come here by car?". Note that for us this sentence has nothing in relation to the intentions. But probably the model will score it as "chance of be in an intention" with small accuracy and will return something like this:

For sentence "Did you come here by car?" the most significant intentions that I've found:

  • Goodbye: 5% of accuracy
  • Greeting: 3% of accuracy

So with this correlation the chatbot probably will decide to answer you: "Bye!". But why? It isnt better to stay silence if you dont know what to answer? Probably, but in a shallow explanation he prefers to reply you with the best answer that he thinks and at least you wont think he doesnt have interest in you.

And if I train well a bot its the job done?

Most of time no. Even if he starts performing well there is a good chance to lost the accuracy by the time. Just compare the different ways to talk between old people and young people, the language is alive, all time we chance our way to talk and for the chatbots this end in: need new data to train or/and new techniques do predict. So is common to regularly monitoring the models performance and retrain the model with new data. THATS MY CASE NOW! Help my chatbots to talk!

If this article helps you or you like the content vote:
This application is at the current contest on open exchange, you can vote in my application iris-python-suite here

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Announcement
· Jun 4, 2020

New Video: InterSystems Health Connect – New and Next

Hi Community,

The new video from Global Summit 2019 is already on InterSystems Developers YouTube:

⏯ InterSystems Health Connect: New and Next

 

In this video, we talk about recent and upcoming advances for InterSystems HealthShare Health Connect.

Takeaway: Health Connect lets me easily connect to all patient information sources.

Presenter: @Craig Lee, Product Specialist, InterSystems

Additional materials to this video you can find in this InterSystems Online Learning Course.

Enjoy watching this video! 👍🏼

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Article
· Jun 4, 2020 1m read

Easy data import into IRIS

Sometimes you need quickly and easily import data into IRIS. For this, an IRIS import manager has been developed.

This application allows you to import JSON data and also provides a really simple interface for transferring data from MongoDB collections to IRIS globals. It has never been so easy.

Let's look at examples.

Import JSON

Suppose we have data in JSON format and we want to import it into IRIS. To do this, you just need to specify the global name and insert the data. After import, you will see the imported data. If global exists data will be overwritten.

 

Import MongoDB collections

 

There is a collection navigation on the main page.

 

Just open the collection and you will see information about it and import control buttons that allow you to transfer data to IRIS or clear it.

After import, you will also see the imported data.

 

What other imports would you like to have in this application? Any ideas are very welcome.

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