This tag relates to the discussions on the development of analytics and business intelligence solutions, visualization, KPI and other business metrics management.
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.
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:
The following post outlines a more flexible architectural design for DeepSee. As in the previous example, this implementation includes separate databases for storing the DeepSee cache, DeepSee implementation and settings, and synchronization globals. This example introduces one new databases to store the DeepSee indices. We will redefine the global mappings so that the DeepSee indices are not mapped together with the fact and dimension tables.
When analyzing data, there is often a need to look at specific indicators more thoroughly and to highlight sections of information of particular interest to a user.
For instance, examining the data dynamics for specific regions or dates can help us uncover some hidden trends and patterns that will allow us to make an informed decision about our project in the future.
In the previous part of this series, we saw how to reference a web page that will enhance our dashboard experience. Now we will look into referencing data that is already in our cubes.
In this example, we will be referencing the controller object and we will be extracting data from it. This data will then be displayed as text in our Dashboard. In Part 5, we will show how to incorporate this data into other charting libraries.
System Monitor is a flexible and highly configurable tool supplied with Caché (Ensemble, HealthShare), which collects the essential metrics of the operating system and Caché itself. System Monitor also notifies administrators about issues with Caché and the operating system, when one or several parameters reach the admin-defined thresholds.
Users of analytical applications often need to generate and send out PDF reports comprised of elements of the analytical panel. In the InterSystems stack, this task is solved using the DSW Reports project that is an extension of DeepSeeWeb. In this article, we will explain how to use DSW Reports for generating PDF reports and emailing them.
This error is sometimes seen while viewing a listing in InterSystems IRIS Business Intelligence: ERROR #5540: SQLCODE: -99 Message: User <USERNAME> is not privileged for the operation (4)
As the error suggests, this is due to a permission error. To figure out which permissions are missing/needed, we can take a look at the SQL query that is generated. We will use a query from SAMPLES as an example.
Running predictive models natively in an InterSystems IRIS Business Process has of course always been the goal of our PMML support, but somehow never made it into the kit because there were a few dependencies and choices that needed addressing and answering. Anyhow, thanks to some pushing and code kindly provided by @Amir Samary (Thanks again Amir!), we finally got it wrapped in a GitHub repo for your enjoyment, review and suggestions.
Web Crawling is a technique used to extract root and related content (HTML, Videos, Images, etc.) from websites to your local disk. This is allows you apply NLP to analyze the content and get important insights. This article detail how to do web crawling and NLP.
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.
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".
In the previous part of this series, we saw how to define a basic portlet. Now we will look into making this portlet reference a web page that will enhance our dashboard experience.
In this example, we will be embedding a Developer Community article along side a couple of widgets displaying information related to the number of views on the Developer Community articles. This example is not hosted on the Community Analytics server, but if it was we could see the view counts going up as we interacted with the page.
Why use this?
In a real case, perhaps you have an embedded page from an external web site showing the current Emergency Room wait times for Hospitals in your area. This portlet can be used along side widgets from your Emergency Room showing how many people are waiting, how many doctors are active, and how many people are being treated. As other Emergency Room wait times grow, you can possibly expect your volume to increase as well. This can help you make decisions on how to allocate resources.
By likes
Open Exchange app.png)
.png)
.png)
