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The following example illustrates how window aggregate functions compute values when there are duplicates, that is, when multiple rows are returned for a single ordering value. The query retrieves the quantity sold to several customers during a specified time range. (Although an inline view was used to define the base data set, it has no special significance and can be ignored.) The query defines a moving window that runs from the date of the current row to 10 days earlier.Note that the RANGE keyword is used to define the windowing clause of this example. This means that the window can potentially hold many rows for each value in the range. In this case, there are three pairs of rows with duplicate date values.
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The APPROX_COUNT_DISTINCT function returns the approximate number of rows containing a distinct value for a specified expression. The APPROX_COUNT_DISTINCT_DETAIL and APPROX_COUNT_DISTINCT_AGG functions enable you to compute varying aggregated levels of approximate distinct value counts within specified groupings. The result of these aggregations can be stored in tables or materialized views for further analysis or answering user queries.
Another approach to computing the approximate number of distinct products sold each year could be to use the APPROX_COUNT_DISTINCT_AGG to aggregate the monthly detail stored in the SALES_APPROX_MONTH table and store the results in a table or materialized view.
Oracle offers a facility for creating your own functions, called user-defined aggregate functions. These functions are written in programming languages such as PL/SQL, Java, and C, and can be used as analytic functions or aggregates in materialized views. See Oracle Database Data Cartridge Developer's Guide for further information regarding syntax and restrictions.
The data returned by business intelligence queries is often most usable if presented in a crosstabular format. The pivot_clause of the SELECT statement lets you write crosstabulation queries that rotate rows into columns, aggregating data in the process of the rotation. Pivoting is a key technique in data warehouses. In it, you transform multiple rows of input into fewer and generally wider rows in the data warehouse. When pivoting, an aggregation operator is applied for each item in the pivot column value list. The pivot column cannot contain an arbitrary expression. If you need to pivot on an expression, then you should alias the expression in a view before the PIVOT operation. The basic syntax is as follows:
To illustrate unpivoting, first create a pivoted table that includes four columns, for quarters of the year. The following command creates a table based on the view sales_view created as described in Example 19-26:
Data is normally stored in sparse form. That is, if no value exists for a given combination of dimension values, no row exists in the fact table. However, you may want to view the data in dense form, with rows for all combination of dimension values displayed even when no fact data exist for them. For example, if a product did not sell during a particular time period, you may still want to see the product for that time period with zero sales value next to it. Moreover, time series calculations can be performed most easily when data is dense along the time dimension. This is because dense data will fill a consistent number of rows for each period, which in turn makes it simple to use the analytic windowing functions with physical offsets. Data densification is the process of converting sparse data into dense form.
In the FROM clause of the inline view dense_sales, you use a partitioned outer join of aggregate view v and time view t to fill gaps in the sales data along the time dimension. The output of the partitioned outer join is then processed by the analytic function SUM ... OVER to compute the weekly year-to-date sales (the weekly_ytd_sales column). Thus, the view dense_sales computes the year-to-date sales data for each week, including those missing in the aggregate view s. The inline view year_over_year_sales then computes the year ago weekly year-to-date sales using the LAG function. The LAG function labeled weekly_ytd_sales_prior_year specifies a PARTITION BY clause that pairs rows for the same week of years 2000 and 2001 into a single partition. You then pass an offset of 1 to the LAG function to get the weekly year to date sales for the prior year.The outermost query block selects data from year_over_year_sales with the condition yr = 2001, and thus the query returns, for each product, its weekly year-to-date sales in the specified weeks of years 2001 and 2000.
The materialized view shown in the following may already exist in your system; if not, create it now. If you must generate it, note that you limit the query to just two products to keep processing time short:
Because this view is limited to two products, it returns just over 2200 rows. Note that the column Hierarchical_Time contains string representations of time from all levels of the time hierarchy. The CASE expression used for the Hierarchical_Time column appends a marker (_0, _1, ...) to each date string to denote the time level of the value. A _0 represents the year level, _1 is quarters, _2 is months, and _3 is day. Note that the GROUP BY clause is a concatenated ROLLUP which specifies the rollup hierarchy for the time and product dimensions. The GROUP BY clause is what determines the hierarchical cube contents.
edge_time is the source for filling time gaps in the hierarchical cube using a partitioned outer join. The column Hierarchical_Time in edge_time will be used in a partitioned join with the Hierarchical_Time column in the view cube_prod_time. The following statement defines edge_time:
The materialized view definition is a duplicate of the view cube_prod_time defined earlier. Because it is a duplicate query, references to cube_prod_time will be rewritten to use the mv_prod_time materialized view. The following materialized may already exist in your system; if not, create it now. If you must generate it, note that you limit the query to just two products to keep processing time short.
The following example performs all four of these calculations. It uses a partitioned outer join of the views cube_prod_time and edge_time to create an inline view of dense data called dense_cube_prod_time. The query then uses the LAG function in the same way as the prior single-level example. The outer WHERE clause specifies time at three levels: the days of August 2001, the entire month, and the entire third quarter of 2001. Note that the last two rows of the results contain the month level and quarter level aggregations.Note that to make the results easier to read if you are using SQL*Plus, the column headings should be adjusted with the following commands. The commands will fold the column headings to reduce line length:
The solution has two steps. Note that you will build this solution using the views and tables created in the prior example. Two steps are required. First, create a view with the new member added to the appropriate dimension. The view uses a UNION ALL operation to add the new member. To query using the custom member, use a CASE expression and a partitioned outer join.
In this statement, the view time_c is defined by performing a UNION ALL of the edge_time view (defined in the prior example) and the user-defined 13th month. The gid_t value of 8 was chosen to differentiate the custom member from the standard members. The UNION ALL specifies the attributes for a 13th month member by doing a SELECT from the DUAL table. Note that the grouping id, column gid_t, is set to 8, and the quarter number is set to 5.
Then, the second step is to use an inline view of the query to perform a partitioned outer join of cube_prod_time with time_c. This step creates sales data for the 13th month at each level of product aggregation. In the main query, the analytic function SUM is used with a CASE expression to compute the 13th month, which is defined as the summation of the first month's sales of each quarter.
The SUM function uses a CASE to limit the data to months 1, 4, 7, and 10 within each year. Due to the tiny data set, with just 2 products, the rollup values of the results are necessarily repetitions of lower level aggregations. For more realistic set of rollup values, you can include more products from the Game Console and Y Box Games subcategories in the underlying materialized view.
Using this view, a marketing program manager can perform an analysis such as "Is this sales-marketing model reasonable for year 2004 data? That is, is the multiple-correlation greater than some acceptable value, say, 0.9?" The SQL for such a query might be as follows:
You can limit the rows returned from SQL queries by either a specific number of rows or a percentage of rows. In some cases, you may need the query results to be ordered before the number of rows returned is limited. A query which first sorts its rows and then limits the number of rows returned is often called a Top-N query, and it offers a straightforward way of creating reports or just a simple view of basic questions, such as "Who are the ten highest-paid employees?" It is also useful for user interfaces that provide the first few rows of a data set for browsing. When you issue a Top-N query, you may also want to specify an offset: the offset excludes the leading rows of the query result set. The query then returns the specified number or percent of rows starting with the first row after the offset. An offset enables you to modify typical questions, so that the question about highest-paid employees might skip the top ten employees and return only those from eleventh to twentieth place in the salary rankings. In a similar manner, you could query the employees by salary, skip the top ten employees and then return the top 10% of the remaining employees.
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