What is Unstructured Data?
Unstructured data refers to information lacking a predefined data model or organization. In contrast to structured data found in databases with clear structures (e.g., tables and fields), unstructured data lacks a fixed schema. This type of data includes text, images, videos, audio files, social media posts, emails, and more.

We continue with the series of articles related to the QuinielaML application. In this article we are going to cover how to prepare the raw data that we have captured using the Embedded Python functionality.

Welcome everybody!

Before we start talking about databases and different data models that exist, first we'd better talk about what a database is and how to use it.

A database is an organized collection of data stored and accessed electronically. It is used to store and retrieve structured, semi-structured, or raw data which is often related to a theme or activity.

At the heart of every database lies at least one model used to describe its data. And depending on the model it is based on, a database may have slightly different characteristics and store different types of data.

To write, retrieve, modify, sort, transform or print the information from the database, a software called Database Management System (DBMS) is used.

The size, capacity, and performance of databases and their respective DBMS have increased by several orders of magnitude. It has been made possible by technological advances in various areas, such as processors, computer memory, computer storage, and computer networks. In general, the development of database technology can be divided into four generations based on the data models or structure: navigational, relational, object and post-relational.

It sometimes happens that due to an adverse event the AUDIT database (IRISAUDIT) has grown to such proportions that the disk it resides on is full and the daily purge cannot be expected to reclaim disk space.

Is anyone like me, and felt really jealous that they didn't have enough points to acquire the IRIS-based Raspberry Pi system when it was offered? Do you have a spare Raspberry Pi 4 handy? If so, I'll walk you through setting up Docker and IRIS on your Raspberry Pi so you can have the smallest IRIS computer in town!

Things you'll need:

This is a self contained class that can be run from the Intersystems Task Scheduler which records peak usage details for databases and licenses built up throughout the day and retaining 30 days history.

To schedule the task to run every hour:

d ##class(Metrics.Task).Schedule()

You can also specify your own start time, stop time, and run interval:

d ##class(Metrics.Task).Schedule(startTime, stopTime, intervalMins)

Metrics are stored in ^Metrics in the namespace that the class resides in/is run from.

This GitHub is the simplest way to scrap using IRIS and Python, all of that already incorporated in an IRIS PRODUCTION.

From here you can build any IRIS production in full Python or in ObjectScript as this module is interoperable.

See for more information

In this GitHub we fine tune a bert model from HuggingFace on review data like Yelp reviews.

The objective of this GitHub is to simulate a simple use case of Machine Learning in IRIS :
We have an IRIS Operation that, on command, can fetch data from the IRIS DataBase to train an existing model in local, then if the new model is better, the user can override the old one with the new one.
That way, every x days, if the DataBase has been extended by the users for example, you can train the model on the new data or on all the data and choose to keep or let go this new model.

Hi Community,

Hi Community,

This post is a introduction of my openexchange iris-python-apps application. Build by using Embedded Python and Python Flask Web Framework.
Application also demonstrates some of the Python functionalities like Data Science, Data Plotting, Data Visualization and QR Code generation.

Features

• Responsive bootstrap IRIS Dashboard

• View dashboard details along with interoperability events log and messages.

• Use of Python plotting from IRIS

• Use of Jupyter Notebook

• Introduction to Data Science, Data Plotting and Data Visualization.

• QR Code generator from python.

Astronomers’ tools

5 years ago, on December 19, 2013, the ESA launched an orbital telescope called Gaia. Learn more about the Gaia mission on the official website of the European Space Agency or in the article by Vitaly Egorov (Billion pixels for a billion stars).

However, few people know what technology the agency chose for storing and processing the data collected by Gaia. Two years before the launch, in 2011, the developers were considering a number of candidates (see “Astrostatistics and Data Mining” by Luis Manuel Sarro, Laurent Eyer, William O’Mullane, Joris De Ridder, pp. 111-112):

• IBM DB2,
• PostgreSQL,
• Apache Hadoop,
• Apache Cassandra and
• InterSystems Caché (to be more precise, the Caché eXTreme Event Persistence technology).

Comparing the technologies side-by-side produced the following results (source):

The first four will probably sound familiar even to schoolchildren. But what is Caché XEP?

Application to import 12 Datasets along with 43 tables dynamically by using LOAD DATA command which loads data from a source into an IRIS SQL table.
List of Datasets