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Note 1: OBIEE offers two calculation types for measure in the repository:

logical

physical

You can define calculations in either of the following ways:

* Before the aggregation, in the logical table source.

* After the aggregation, in a logical column derived from two other logical columns. For example: sum(col A) * sum(col B)

Note 2: A measure column is a column unlike the baseline column that has a default aggregation rule defined in the Aggregation tab of the Logical Column dialog in the repository.

Measure columns always calculate the aggregation with which they are defined.

Measures need to be defined in a logical fact. Any column with an aggregation rule is a measure.

B: The problem with caching of any kind is how to deal with stale data. How you decide to deal with this depends on how time critical your business intelligence queries happen to be. OBIEE provides a wide variety of mechanisms for dealing with stale data. The cache can be purged automatically at intervals. Caching can be enabled selectively for individual physical tables, so that data in a frequently updated table is never cached. OBIEE provides a set of ODBC procedures that can be called when ETL processes complete to programmatically purge designated tables. But, by far the best way to selectively purge the cache is to use an event polling table. This table is populated by the client database whenever changes are made to any table (by an ETL process for a data warehouse or by a table-based trigger for an OLTP database). At specified intervals the BI Server polls this event polling table and deletes from its cache any query results that are dependent on the changed data.

B: Example:

1. Configure the new source table to have the 'logical level' under the content set to 'Countries' (the summarized level)

b

2. Do the similiar setting for dimension table

Now when user query the data at 'Counties' level, the query will be redirected to these summary table instead of the more detail fact table

Note: Aggregate tables are Physical tables that store Aggregates of measures across multiple levels of a Hierarchy.

Creating Logical Time Dimensions

Compared to modeling an ordinary dimension, the time dimension requires just two additional steps: selecting the Time option in the Logical Dimension dialog, and designating a chronological key for every level of every dimension hierarchy.

Note: Time dimensions and chronological keys. You can identify a dimension as a time dimension. At least one level of a time dimension must have a chronological key. The following is a list of some guidelines you should use when setting up and using time dimensions:

* At least one level of a time dimension must have a chronological key.

* All time series measures using the AGO, TODATE, and PERIODROLLING functions must be on time levels. AGO, TODATE, and PERIODROLLING aggregates are created as derived logical columns.

Steps to Create a Dimension Hierarchy - >Create a dimension object.Add a parent-level object.Add child-level objects.Determine number of elements.Specify level columns.Create level keys.Create a level-based measure.Create additional level-based measures.Create share measures.Create rank measures.Add measures to Presentation layer.Test share and rank measures.