This tag relates to the discussions on the development of analytics and business intelligence solutions, visualization, KPI and other business metrics management.
A full cycle project, from initial data initialization, daily update products from the official website, a service for sending a course on request and receiving a schedule for any period.
By design, DSW provides an implementation for every widget in DeepSee library. But there are some extra features in DSW which make solutions built with DSW dashboards more functional. This article describes it.
I'm happy to share with the community a web server log dataset from our longtime customer, an operating company.
Their webserver operates on Apache webserver and contains data which can be useful to analyse a load and search engines activity.
After installing the project, you will get the data for a few months that can show a typical load and activity of clients, robots and also you can see how it depends on day of week, holidays and time of a day.
With the improvement of living standards, people pay more and more attention to physical health. And the healthy development of children has become more and more a topic of concern for parents. The child's physical development can be reflected from the child's height and weight. Therefore, it is of great significance to predict the height and weight in a timely manner.
Why log in to view pivot tables when you can have them delivered directly to your inbox? Are you in need of automated reporting for InterSystems IRIS Business Intelligence? Pivot Subscriptions is your solution: a tool for subscribing to scheduled email reports of pivot tables inside InterSystems IRIS Business Intelligence (previously known as DeepSee).
A few months ago I touched on a brief note on "Python JDBC connection into IRIS", and since then I referred to it more frequently than my own scratchpad hidden deep in my PC. Hence, here comes up another 5-minute note on how to make "Python ODBC connection into IRIS".
I was using PowerBI to create regular display data obtained from one popular web sourse with hundreds of thousands of visitors per month and a big number of users.
At the beginning of that visualisation development, I was using direct connection from Power BI to Adaptive Analytics powered by AtScale. Adaptive Analytics is useful for cached data, aggregates and fast data sources switching between development and stage phases. The “AtScale cubes'' connection method was used:
When I started this project I had set myself limits: Though there is a wide range of almost ready-to-use modules in various languages and though IRIS has excellent facilities and interfaces to make use of them I decided to solve the challenge "totally internal" just with embedded Python, SQL, ObjectScript Neither Java, nor Nodes, nor Angular, PEX, ... you name it. The combination of embedded Python and SQL is preferred. ObjectScript is just my last chance.
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.
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.
Now available on Open Exchange is a library of third party charts available to use within DeepSee/InterSystems IRIS BI dashboards. To start, simply download and install, select the new portlet as the widget type, then select the chart type that you desire. If you don't find the type of chart you are looking for, you can easily extend the portlet to implement your desired chart type. These new chart types can be used within existing dashboards or you can create new dashboards using them.
As said in the previous article about the iris-fhir-generative-ai experiment, the project logs all events for analysis. Here we are going to discuss two types of analysis covered by analytics embedded in the project:
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