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AGGREGATION → Within a model, the AGGREGATION function allows you to create a model that represents a custom … aggregate. Such an aggmap can be used for dynamic aggregation with the AGGREGATE function.
Note: Because the AGGREGATION function is intended only for dynamic aggregation, a model that contains… → A list of one or more dimension values to include in the custom aggregation. The specified values … a variable. Examples Example 7-9 Using the AGGREGATION Function to Create a Custom Aggregate The … specification of the model using the AGGREGATION function. DEFINE mytime_custagg MODEL MODEL JOINLINES … ('DIMENSION time' 'My_Time
Using SQL Aggregation Management → SQL Aggregation Management is a group of PL/SQL subprograms in DBMS_CUBE that supports the rapid
Considerations when Using Aggregation → This section discusses the following topics. Hierarchy Handling in ROLLUP and CUBE Column Capacity in ROLLUP and CUBE HAVING Clause Used with GROUP BY Extensions ORDER BY Clause Used with GROUP BY Extensions Using Other Aggregate Functions with ROLLUP and CUBE
aggregation → aggregation is synonymous with summarization, and aggregate data is synonymous with summary data.
Example of SQL Aggregation Management → All examples for the SQL Aggregate Management subprograms use the sample Sales History schema, which is installed in Oracle Database with two relational materialized views: CAL_MONTH_SALES_MV and FWEEK_PSCAT_SALES_MV.
Aggregation Level Bit Vector GROUPING_ID → a, b 0 0 0 a 0 1 1 b 1 0 2 Grand Total 1 1 3 GROUPING_ID clearly distinguishes groupings created by grouping set specification, and it is very useful during refresh and rewrite of materialized views.
Subprograms in SQL Aggregation Management → These subprograms are included in SQL Aggregation Management: CREATE_MVIEW Function
Aggregation Improvements → In Oracle Database 11 g, the following changes have been made to enhance aggregation: Aggregation
Aggregation Operators → Analytic workspaces provide an extensive list of aggregation methods, including weighted, hierarchical, and weighted hierarchical methods.
21 SQL for Aggregation in Data Warehouses → This chapter discusses aggregation of SQL, a basic aspect of data warehousing. It contains these … topics: Overview of SQL for Aggregation in Data Warehouses ROLLUP Extension to GROUP BY CUBE Extension … Considerations when Using Aggregation Computation Using the WITH Clause Working with Hierarchical Cubes in SQL
An Aggregate Scenario → To illustrate the use of the GROUP BY extension, this chapter uses the sh data of the sample schema. All the examples refer to data from this scenario. The hypothetical company has sales across the world and tracks sales by both dollars and quantities information. Because there are many rows of data, the queries shown here typically have tight constraints on their WHERE clauses to limit the results
When to Use ROLLUP → Use the ROLLUP extension in tasks involving subtotals. It is very helpful for subtotaling along a hierarchical dimension such as time or geography. For instance, a query could specify a ROLLUP(y, m, day) or ROLLUP(country, state, city). For data warehouse administrators using summary tables, ROLLUP can simplify and speed up the maintenance of summary tables.
When to Use CUBE → Consider Using CUBE in any situation requiring cross-tabular reports. The data needed for cross-tabular reports can be generated with a single SELECT using CUBE. Like ROLLUP, CUBE can be helpful in generating summary tables. Note that population of summary tables is even faster if the CUBE query executes in parallel. CUBE is typically most suitable in queries that use columns from multiple dimensions
Specifying Hierarchical Cubes in SQL → Oracle Database can specify hierarchical cubes in a simple and efficient SQL query. These hierarchical cubes represent the logical cubes referred to in many analytical SQL products. To specify data in the form of hierarchical cubes, you can use one of the extensions to the GROUP BY clause, concatenated grouping sets, to generate the aggregates needed for a hierarchical cube of data. By using concatenated
ROLLUP Syntax → rounding. This query returns the following sets of rows: Regular aggregation rows that would be
GROUPING SETS Expression → You can s electively specify the set of groups that you want to create using a GROUPING SETS expression within a GROUP BY clause. This allows precise specification across multiple dimensions without computing the whole CUBE. For example, you can say: SELECT channel_desc, calendar_month_desc, country_iso_code, TO_CHAR(SUM(amount_sold), '9,999,999,999') SALES$ FROM sales, customers, times, channels,
Concatenated Groupings → Concatenated groupings offer a concise way to generate useful combinations of groupings. Groupings specified with concatenated groupings yield the cross-product of groupings from each grouping set. The cross-product operation enables even a small number of concatenated groupings to generate a large number of final groups. The concatenated groupings are specified simply by listing multiple grouping
Working with Hierarchical Cubes in SQL → This section illustrates examples of working with hierarchical cubes.
Overview of SQL for Aggregation in Data Warehouses → Aggregation is a fundamental part of data warehousing. To improve aggregation performance in your … aggregation, from the most detailed up to a grand total. CUBE is an extension similar to ROLLUP