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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
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
Using SQL Aggregation Management → SQL Aggregation Management is a group of PL/SQL subprograms in DBMS_CUBE that supports the rapid
aggregation → aggregation is synonymous with summarization, and aggregate data is synonymous with summary data.
Subprograms in SQL Aggregation Management → These subprograms are included in SQL Aggregation Management: CREATE_MVIEW Function
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.
Aggregation Operators → Analytic workspaces provide an extensive list of aggregation methods, including weighted, hierarchical, and weighted hierarchical methods.
Aggregation Improvements → In Oracle Database 11 g, the following changes have been made to enhance aggregation: Aggregation
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
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
Querying Hierarchical Cubes in SQL → the specific group each row belongs to, based on the aggregation level of the grouping-columns in … unneeded aggregation groups from query processing based on the outer query conditions. The outer … that each dimension's aggregation hierarchy is precomputed in combination with each of the other
ORDER BY Clause Used with GROUP BY Extensions → -aggregate columns. This requirement means that queries using ORDER BY along with aggregation extensions
GROUPING SETS Statement Equivalent GROUP BY Statement → GROUP BY GROUPING SETS(a, b, c) GROUP BY a UNION ALL GROUP BY b UNION ALL GROUP BY c GROUP BY GROUPING SETS(a, b, (b, c)) GROUP BY a UNION ALL GROUP BY b UNION ALL GROUP BY b, c GROUP BY GROUPING SETS((a, b, c)) GROUP BY a, b, c GROUP BY GROUPING SETS(a, (b), ()) GROUP BY a UNION ALL GROUP BY b UNION ALL GROUP BY () GROUP BY GROUPING SETS(a, ROLLUP(b, c)) GROUP BY a UNION ALL GROUP BY ROLLUP(b, c)
GROUPING_ID Function → To find the GROUP BY level of a particular row, a query must return GROUPING function information for each of the GROUP BY columns. If we do this using the GROUPING function, every GROUP BY column requires another column using the GROUPING function. For instance, a four-column GROUP BY clause must be analyzed with four GROUPING functions. This is inconvenient to write in SQL and increases the number
Computation Using the WITH Clause → The WITH clause (formally known as subquery_factoring_clause ) enables you to reuse the same query block in a SELECT statement when it occurs more than once within a complex query. WITH is a part of the SQL-99 standard. This is particularly useful when a query has multiple references to the same query block and there are joins and aggregations. Using the WITH clause, Oracle retrieves the results of
Partial CUBE → resembles partial ROLLUP in that you can limit it to certain dimensions and precede it with columns outside the CUBE operator. In this case, subtotals of all possible combinations are limited to the dimensions within the cube list (in parentheses), and they are combined with the preceding items in the GROUP BY list. The syntax for partial CUBE is as follows: GROUP BY expr1, CUBE(expr2, expr3) This
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,
Analyzing Across Multiple Dimensions → multidimensional requests: Show total sales across all products at increasing aggregation levels for a … dimensions are vital to multidimensional analyses. Therefore, analytical tasks require convenient and efficient data aggregation.