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 Post subject: BI using VISTA
PostPosted: Tue Nov 29, 2011 9:34 pm 

Joined: Thu Aug 18, 2011 3:45 am
Posts: 2
Real Name: Sumit Saurav
Began Programming in MUMPS: 05 Aug 2011

I am new to vistA and GT.M. I am interested in using BI tools for vistA and GTM implementation at my workplace. How can I directly build cubes from GT.M instead of taking data to an RDBMS?


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 Post subject: Re: BI using VISTA
PostPosted: Mon Nov 05, 2012 3:34 pm 
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Joined: Wed Nov 17, 2010 4:02 pm
Posts: 68
Location: Houston TX
Real Name: David Whitten
Began Programming in MUMPS: 06 Jan 1982
I assume BI stands for Business Intelligence. I assume when you say "build cubes" you are talking about online analytic processing (OLAP) cubes such as those discussed here: ... eatemd.htm

Lessons in this tutorial
Optional: Start the tutorial here
You can skip the previous modules and start the tutorial here by completing a few short steps. If you already completed the previous modules, do not complete these steps; go to Lesson 1.
Lesson 1: Creating a complete cube model
In this lesson you build a cube model for OLAP analysis that describes relationships in the relational data. The cube model enables you to define the aspects of the data that are important for the analytic needs of JK Superstore.
Lesson 2: Adding a hierarchy to the Time dimension
In this lesson, you add the Calendar Year hierarchy to the set of OLAP metadata that you imported in Lesson 1.
Lesson 3: Creating a cube
In this lesson, you create a cube called Price Analysis that includes the metrics and dimensions that are needed by the JK Superstore business analysts. Cubes are a critical component of your OLAP metadata and are used by reporting applications, like Alphablox.
Lesson 4: Deploying your OLAP metadata to the DWESAMP sample database
In this lesson you validate the model, and then deploy your metadata to a database.
Lesson 5: Creating MQT recommendations using the Optimization Advisor wizard
In this lesson you use the Optimization Advisor wizard to create SQL scripts that can build a set of recommended materialized query tables (MQTs) for a cube model. MQTs aggregate commonly accessed data to speed up query performance for applications that issue SQL-based, OLAP-style queries, such as Alphablox.
Lesson 6: Deploying your recommended MQTs
In this lesson, you use the DWE Administration Console to run the SQL script to create the recommended MQTs that you designed in the Design Studio.

It might be interesting to see what is necessary to integrate OLAP Cubes into VistA. It might involve the existing definitions and meta-data, and it might require more.

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