Although Global is used for most of the examples in this manual, Sales History has a very different set of data characteristics and demonstrates a correspondingly different set of build choices.
Sales History (SH) is a sample star schema that is delivered with your Oracle Database, along with a fully defined logical model stored in the OLAP Catalog. The SH schema has two cubes, SALES and COSTS. The SALES cube has five dimensions, and the COSTS cube uses two of these dimensions. This case study uses only the SALES cube.
Defining Startup Parameters for the SH Build
When building a large analytic workspace, the startup parameters for the Oracle Database affect how quickly the build proceeds. Example shows a few of the settings in the init.ora file for building Sales History.
Example Startup Parameters for Building Sales History
Defining Tablespaces for SH
While the GLOBAL analytic workspace has less than a million cells for base-level data in its largest cube, the Sales History COST cube has over 235 trillion. This makes Sales History quite large for a sample schema, yet it is small to average for a real application. It is sufficiently large for the build to fail unless resources have been allocated specifically for its use. The build needs adequate temporary and permanent tablespaces:
Example SQL Script for Defining Tablespaces for the Sales History Analytic Workspace
Examining the Sparsity Characteristics of SH Data
The data in the SH relational schema is extremely sparse. Many dimension keys are never used as foreign keys in the SALES fact table, much less used in all possible combinations with the other four dimensions. For example, CUSTOMERS.CUST_ID has 5100 values, of which only 2557 are used in the SALES.CUST_ID column. Time is also a sparse dimension, with only 1075 of 1826 dimension members used. Thus, TIMES_DIM must be included in the composite. You can define a composite with all five dimensions by choosing Advanced Storage Options. List TIMES as the first dimension (the fastest varying) in the composite, to facilitate time-based analysis and data maintenance, even though it is smaller than PRODUCTS and CUSTOMERS. List the other dimensions from largest to smallest. This information is easily obtained by issuing a SELECT COUNT(*) on the dimension tables.
Managing the SH Build
Because SH is large, you may want to manage these aspects of the build:
Running the Create Analytic Workspace Wizard
Make these choices in the Create Analytic Workspace wizard for building Sales History, based on the previous discussion:
Building the Sales History Analytic Workspace
Take these steps to build the Sales History analytic workspace:
The workspace can be enabled now or after deploying an aggregation plan.
OLAP Related Interview Questions
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OLAP Related Practice Tests
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|Hyperion Financial Management Practice Tests||Hyperion Essbase 5 Practice Tests|
|Database Design Practice Tests|
The Multidimensional Data Model
The Sample Schema
Developing Java Applications For Olap
Defining A Logical Multidimensional Model
Creating An Analytic Workspace
Sql Access To Analytic Workspaces
Exploring A Standard Form Analytic Workspace
Adding Measures To A Standard Form Analytic Workspace
Predicting Future Performance
Acquiring Data From Other Sources
Administering Oracle Olap
Materialized Views For The Olap Api
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