This article contains the tutorial document for a Global Summit academy session on Text Categorization and provides a helpful starting point to learn about Text Categorization and how iKnow can help you to implement Text Categorization models. This document was originally prepared by Kerry Kirkham and Max Vershinin and should work based on the sample data provided in the SAMPLES namespace.

I've asked a lot of questions leading up to this, so I wanted to share some of my progress.

The blue line represents the number of messages processed. The background color represents the average response time. You can see ticks for each hour (and bigger ticks for each day). Hovering over any point in the graph will show you the numbers for that period in time.

This is super useful for "at a glance" performance monitoring as well as establishing patterns in our utilization.

Whether you are accessing DeepSee for the first time ever or you are configuring DeepSee on a new instance, there are two common issues that are encountered after clicking on the “DeepSee” option in the System Management Portal.

Issue #1: Architect/Analyzer is grayed out!

Issue #2: DeepSee must be enabled before use.

Issue #1: Architect/Analyzer is grayed out!

There are two common causes for this problem.

One of the most important tasks of dashboards is, on the one hand, to help you perceive data in an aggregated form, and, on the other hand, not to lose the depth of immersion in the data if you need this. One of the tools that help us achieve this result is drill down. It enables us to display several hierarchical levels of data, from aggregated to detailed.

Purpose

This set of tools (RanRead, RanWrite, and the combined RanIO) is used to generate random read and write events within a database (or pair of databases) to test the IO speed of IRIS running on a specified hardware setup. While Read operations can be measured in the usual Input/Output operations per second (IOPS) since they're direct disk reads, write events are sent to the database and thus their physical writes are managed by IRIS's write daemon.

Results gathered from the IO tests will vary from configuration to configuration based on the IO sub-system. Before running these tests, ensure corresponding operating system and storage level monitoring are configured to capture IO performance metrics for later analysis. The suggested method is by running the System Performance tool that comes bundled within IRIS. Please note that this is an update to a previous release, which can be found here.

InterSystems OMOP, The Cloud Service (Troy)

The pandemic that struck the world in 2020 made everyone follow the news and the numbers that involve the COVID-19.

Why don’t you take that opportunity to create something simple and pleasant, to follow the number of vaccinations worldwide?

To face this challenge, I'm using the data provided by Our World in Data - Research and data to make progress against the world’s largest problems.

They have a dedicated repository on Github with the data of COVID-19, and I took the vaccination data to help me with my tracker.

The source class of a DeepSee cube has a property referencing a different class:

Class ClassA Extends %Persistent {
     Property P1 As ClassB;
}

When records in class B change, the ^OBJ.DSTIME global for Class A will not be automatically updated. This means that synchronization of cubes based on source class A will not reflect the changes occurred to property P1.
This post will help you determine the best way to achieve synchronization of properties referencing a different class

According to IDC, more than 80% of information it is NoSQL, especially text into documents. When the digital services or applications not process all this information, the business lose. To face this challenge, it is possible use OCR technology. OCR uses machine learning and/or trained image patterns to transform image pixels into text. This is important, because many documents are scanned into images inside PDF, or many documents contains images with text inside. So OCR are an important step to get all possible data from a document.

I really love documentaries! Last weekend I was watching a Netflix documentary called This is Pop, because it was Analytics Contest time and I thought: Why not creating a pop song analytics with InterSystems Iris?

I am planning to implement Business Intelligence based on the data in my instances. What is the best way to set up my databases and environment to use DeepSee?

The following post is a guide to implement a basic architecture for DeepSee. This implementation includes a database for the DeepSee cache and a database for the DeepSee implementation and settings.

A simple data analysis example created in IntegratedML and Dashboard

Based on InterSystems' Integrated ML technology and Dashboard, automatically generate relevant predictions and BI pages based on uploaded CSV files. The front and back ends are completed in Vue and Iris, allowing users to generate their desired data prediction and analysis pages with simple operations and make decisions based on them.

# ZPM installation

zpm:USER>install IntegratedMLandDashboardSample

# Process Deployment

Following up the previous part, it's time to take advantages for IntegratedML VALIDATION MODEL statement, to provide information in order to monitor your ML models. You can watch it in action here