Previously I have already tried to play with Google Data Studio when I connected it to InterSystems FHIRaaS. It has quite a nice UI, with a few chart types available out of the box, it can be quite easily connected to some plain tables (stored as CSV or JSON, for instance), and gives the ability to build quite flexible analytics over it. So, I have decided to implement a new connector to InterSystems Analytics (DeepSee), with the ability to select a cube and do some queries on it.

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Today, is important analyze the content into portals and websites to get informed, analyze the concorrents, analyze trends, the richness and scope of content of websites. To do this, you can alocate people to read thousand of pages and spend much money or use a crawler to extract website content and execute NLP on it. You will get all necessary insights to analyze and make precise decisions in a few minutes.

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Article
Evgeniy Potapov · Nov 15 6m read
How to develop in Logi

Today we will talk about InterSystems Reports. This is a BI system that provides you with tools to create static reports and export them to different file formats. We will see how it works using the DC Analytics public analytical sample as an example. In this article, we will examine how to familiarize yourself with the reports available in the repository, how to make a new report based on a ready-made data structure, and how to prepare a data structure from scratch.

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Hi, Community!

I’m sure you are using Developer Community analytics built with InterSystems Analytics technology DeepSee:

You can find DC analytics n InterSystems->Analytics menu.

DC Analytics shows interactive dashboards on key figures of DC entities: Posts, Comments, and Members.

Since the last week, this analytics project is available for everyone with source code and data on DC Github!

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The InterSystems IRIS has a very nice container class to allows you have your Dashboards as classes source code. It is %DeepSee.UserLibrary.Container.

With this class is possible group all your dashboard and pivot table definitions.

This is useful to automatically create your dashboards when you build your docker project and other automation scenarios.

See:

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Article
Evgeniy Potapov · Jan 14 1m read
Real Webserver Logs Dataset

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.

The Cube is also included in package.

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Hi guys!

I'd like to present you my new project: iris-analytics-notebook, a notebook approach to use IRIS analytics capabilities.

Project description

In past few years, notebooks tools like Jupyter are gaining popularity due its natural way to express ideias.

An almost unipresent tool for data scientists, notebook can also help to improve the impact of analytics tools for all sort of users.

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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.

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I was approached recently by and end use who wanted to perform analysis of their databases and see how they could save some space by picking data good for deletion without harming the application. As part of investigation, they wanted to know sizes of globals within datasets. This can be achieved by various means but all of them provide data in text form only.

I thought I might be a good tool for database administrators in general - to see global sizes in a graphical way.

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A few months ago, I read this interesting article from MIT Technology Review, explaing how COVID-19 pandemic are issuing challenges to IT teams worldwide regarding their machine learning (ML) systems.

Such article inspire me to think about how to deal with performance issues after a ML model was deployed.

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Article
Peter Steiwer · Dec 31, 2019 3m read
Portlets that use data from DeepSee


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.

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In the previous part of this series, we saw how to include data in a portlet from within DeepSee. This used the built in data controller. In this part, we are going to be pulling in data from outside of DeepSee. This will include both information from within InterSystems IRIS and from the OS.

Why use this?

This is useful if you would like to create a dashboard that only contains information about your system. It is also useful if you want to display data about your system along side data that you have stored in DeepSee.

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InterSystems Developer Community analytics. Project made with InterSystems IRIS BI (DeepSee), Power BI and Logi Report Designer to visualize and analyze members, articles, questions, answers, views and other pieces of content and activity on InterSystems Developer Community.

You can see your own activity, articles and questions. Track how your contribution changes developer community.

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Hello Community,

Thank you all for your continued feedback and support of our ad hoc reporting platform, VDM. There's been some questions around setting up a non-ODBC connection for InterSystems platforms. We published a new YouTube video showing the steps necessary to connect to InterSystems Caché and InterSystem IRIS with BridgeWorks VDM.

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Article
Yuri Marx · Nov 20, 2020 2m read
Enrich your analytics projects with NLP

According IDC, 80% of all data produced are NoSQL. See:

There are digital documents, scanned documents, online and offline texts, blob content into SQL, images, videos and audio. Imagine a Corporate Analytics initiative without all these data to analyze and support decisions?

In all the world, many projects are using techonologies to transform these NoSQL data into textual content, to allows analyze it. See:

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Preview Mode was added to InterSystems IRIS Business Intelligence to give designers a quick view of what their resulting Pivot Table will look like without needing to wait for the results to fully execute. This can be beneficial when designing pivot tables because if you are dragging and dropping elements to see how they look/work in your pivot table and seeing if they have the desired data. Since you are exploring and designing, you don't necessarily care about the results at the moment, but you would still like to see how your table looks with the changes you have made.

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With the release of InterSystems IRIS, we're also making available a nifty bit of software that allows you to get the best out of your InterSystems IRIS cluster when working with Apache Spark for data processing, machine learning and other data-heavy fun. Let's take a closer look at how we're making your life as a Data Scientist easier, as you're probably already facing tough big data challenges already, just from the influx of job offers in your inbox!

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Hi, Community!

This is the 3rd part of DeepSee Web story - Angular base UI for DeepSee Dashboards, see the beginning here.

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.

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InterSystems IRIS Business Intelligence allows you to keep your cubes up to date in multiple ways. This article will cover building vs synchronizing. There are also ways to manually keep cubes up to date, but these are very special cases and almost always cubes are kept current by building or synchronizing.

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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.

To do web crawling you can choose a tool in Java or Python. In my case I'm using Crawler4J. (https://github.com/yasserg/crawler4j).

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Keywords: PyODBC, unixODBC, IRIS, IntegratedML, Jupyter Notebook, Python 3

Purpose

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".

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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.

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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.

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