IS 465Decision Support System

First Tutorial

Data Warehousing

Second Semester 1427-1428H

First Example

Enrollment Data

Class Day Time Prof Enrolled 1336 T 8 Aars 14 1430 M 2 Aars 28 1430 M 11 Booth 30 1430 T 11 Booth 26 1430 T 2 Booth 23 1430 T 2 Fry 27 1430 T 12 Aars 29 1440 M 1 Aars 11 2334 M 9 Fry 27 2350 M 10 Maurer 19 3101 T 12 Grabow 16 3303 M 11 Aars 11 3324 M 8 Gaitros 20 3330 T 11 Fry 5 3331 T 12 Aars 11 3334 M 11 Hamerly 20 3335 M 2 Donahoo 17 3336 T 8 Sturgill 9 3342 T 2 Aars 10 3439 T 9 Poucher 10

Enroll Table

Group by rollup

Select Prof, Sum(Students) From enroll Group by rollup(prof)

Prof Enrolled Aars 114 Booth 79 Donahoo 17 Fry 59 Gaitros 20 Grabow 16 Hamerly 20 Maurer 19 Poucher 10 Sturgill 9

363

Group by cubeSelect Day, Time, Sum(Students)From enrollGroup By cube(Day,Time) {rollup(Day,Time) -- all rows except ([NULL])}

Day Time Enrolled363

[NULL] 1 11 [NULL] 2 105 [NULL] 8 43 [NULL] 9 37 [NULL] 10 19 [NULL] 11 92 [NULL] 12 56 M 183 M 1 11 M 2 45 M 8 20 M 9 27 M 10 19 M 11 61 T 180 T 2 60 T 8 23 T 9 10 T 11 31 T 12 56

Group by rollup

Select Day, Time, Prof, Sum(Students) From enroll Group by rollup(day,time),rollup(prof)

Second Example

Sales Data

Sales Cube

Market

Time

Product

Sales Example (Cont.)Simple Cross-Tabular Report

1997

Region  Department   

 CassetteSales Profit  

Video Sales Profit  

Total Profit  

Central  82,000   85,000  167,000  

East  101,000  137,000  238,000  

West  96,000  97,000  193,000  

Total  279,000  319,000  598,000  

Sales Example (Cont.)Roll up – the query

SELECT Time, Region, Department, sum(Profit)

FROM salesGROUP BY ROLLUP(Time, Region, Dept)

Sales Example (Cont.)Roll up

this query returns the following sets of rows: •Regular aggregation rows that would be produced by GROUP BY without using ROLLUP .

•First-level subtotals aggregating across Department for each combination of Time and Region .

•Second-level subtotals aggregating across Region and Department for each Time value .

•A grand total row .

Sales Example (Cont.)Roll up – the result of query

Time Region Dept Profit1996 Central CassetteSales 75,0001996 Central VideoSales 74,0001996 Central [NULL] 149,0001996 East CassetteSales 89,0001996 East VideoSales 115,0001996 East [NULL] 204,0001996 West CassetteSales 87,0001996 West VideoSales 86,0001996 West [NULL] 173,0001996 [NULL] [NULL] 526,0001997 Central CassetteSales 82,0001997 Central VideoSales 85,0001997 Central [NULL] 167,0001997 East CassetteSales 101,0001997 East VideoSales 137,0001997 East [NULL] 238,0001997 West CassetteSales 96,0001997 West VideoSales 97,0001997 West [NULL] 193,0001997 [NULL] [NULL] 598,000[NULL] [NULL] [NULL] 1,124,000

Calculating Subtotals without ROLLUP

The result set could be generated by the UNION of four SELECT statements, as shown below. This is a subtotal across three dimensions. Notice that a complete set of ROLLUP-style subtotals in n dimensions would require n+1 SELECT statements linked with UNION ALL.

SELECT Time, Region, Department, SUM(Profit) FROM Sales GROUP BY Time, Region, DepartmentUNION ALL SELECT Time, Region, '' , SUM(Profit) FROM Sales GROUP BY Time, RegionUNION ALL SELECT Time, '', '', SUM(Profits) FROM Sales GROUP BY TimeUNION ALL SELECT '', '', '', SUM(Profits) FROM Sales;

Sales Example (Cont.)Roll up

Sales Example (Cont.)Roll up – small exercise

SELECT Time, Region, Department, sum(Profit)

FROM salesGROUP BY ROLLUP(Time, Dept, Region)

Sales Example (Cont.)Cube - the query

SELECT Time, Region, Department, sum(Profit)FROM salesGROUP BY CUBE (Time, Region, Dept)

Time  Region  Dept Profit  1996   Central   CassetteSales   75,000  1996   Central   VideoSales   74,000  1996   Central   [NULL]   149,000  1996   East   CassetteSales   89,000  1996   East   VideoSales   115,000  1996   East   [NULL]   204,000  1996   West   CassetteSales   87,000  1996   West   VideoSales   86,000  1996   West   [NULL]   173,000  1996   [NULL]   CassetteSales   251,000  1996   [NULL]   VideoSales   275,000  1996   [NULL]   [NULL]   526,000  1997   Central   CassetteSales   82,000  1997   Central   VideoSales   85,000  1997   Central   [NULL]   167,000  1997   East   CassetteSales   101,000  1997   East   VideoSales   137,000  1997   East   [NULL]   238,000  1997   West   CassetteSales   96,000  1997   West   VideoSales   97,000  1997   West   [NULL]   193,000  1997   [NULL]   CassetteSales   279,000  1997   [NULL]   VideoSales   319,000  1997   [NULL]   [NULL]   598,000  [NULL]   Central   CassetteSales   157,000  [NULL]   Central   VideoSales   159,000  [NULL]   Central   [NULL]   316,000  [NULL]   East   CassetteSales   190,000  [NULL]   East   VideoSales   252,000  [NULL]   East   [NULL]   442,000  [NULL]   West   CassetteSales   183,000  [NULL]   West   VideoSales   183,000  [NULL]   West   [NULL]   366,000  [NULL]   [NULL]   CassetteSales   530,000  [NULL]   [NULL]   VideoSales   594,000  [NULL]   [NULL]   [NULL]   1,124,000  

Sales Example (Cont.)Cube – the result of query

Calculating Subtotals without CUBE

Just as for ROLLUP, multiple SELECT statements combined with UNION statements could provide the same information gathered through CUBE. However, this may require many SELECT statements: for an n-dimensional cube, 2n SELECT statements are needed. In our 3-dimension example, this would mean issuing 8 SELECTS linked with UNION ALL.

Sales Example (Cont.)Cube

Sales Example (Cont.)Cube – small exercise

SELECT Time, Region, Department, sum(Profit)FROM salesGROUP BY CUBE (Time, Dept, Region)

Sales Example (Cont.)Grouping

Two challenges arise with the use of ROLLUP and CUBE. First, how can we programmatically determine which result set rows are subtotals, and how do we find the exact level of aggregation of a given subtotal? We will often need to use subtotals in calculations such as percent-of-totals, so we need an easy way to determine which rows are the subtotals we seek. Second, what happens if query results contain both stored NULL values and "NULL" values created by a ROLLUP or CUBE? How does an application or developer differentiate between the two?

Sales Example (Cont.)Grouping

To handle these issues, we have a function called GROUPING. Using a single column as its argument, Grouping returns 1 when it encounters a NULL value created by a ROLLUP or CUBE operation. That is, if the NULL indicates the row is a subtotal, GROUPING returns a 1. Any other type of value, including a stored NULL, will return a 0.

Sales Example (Cont.)Grouping – the query

SELECT Time, Region, Department, SUM(Profit) GROUPING (Time) as T, GROUPING (Region) as R, GROUPING (Department) as D FROM SalesGROUP BY ROLLUP (Time, Region, Department)

Time  Region  Dept  Profit  T  R  D 1996   Central   CassetteSales  75,000   0   0   0  1996   Central   Video Sales   74,000   0   0   0  1996   Central   [NULL]   149,000   0   0   1  1996   East   CassetteSales  89,000   0   0   0  1996   East   Video Sales   115,000   0   0   0  1996   East   [NULL]   204,000   0   0   1  1996   West   CassetteSales  87,000   0   0   0  1996   West   Video Sales   86,000   0   0   0  1996   West   [NULL]   173,000   0   0   1  1996   [NULL]   [NULL]   526,000   0   1   1  1997   Central   CassetteSales  82,000   0   0   0  1997   Central   Video Sales   85,000   0   0   0  1997   Central   [NULL]   167,000   0   0   1  1997   East   CassetteSales  101,000   0   0   0  1997   East   Video Sales   137,000   0   0   0  1997   East   [NULL]   238,000   0   0   1  1997   West   VideoRental   96,000   0   0   0  1997   West   VideoSales   97,000   0   0   0  1997   West   [NULL]   193,000   0   0   1  1997   [NULL]   [NULL]   598,000   0   1   1  [NULL]   [NULL]   [NULL]   1,124,000   1   1   1  

Sales Example (Cont.)Grouping – the result of query

Grouping

Time  Region  Profit  1996   East   200,000  1996   [NULL]   200,000  [NULL]   East   200,000  [NULL]   [NULL]   190,000  [NULL]   [NULL]   190,000  [NULL]   [NULL]   190,000  [NULL]   [NULL]   390,000  

This table shows an ambiguous result set created using the CUBE extension.

Grouping (Cont.)We can resolve the ambiguity by using the GROUPING and other

functions in the code below

SELECT decode(grouping(Time), 1, 'All Times', Time) as Time, decode(grouping(region), 1, 'All Regions', 0, null)) as Region, sum(Profit)

FROM Sales GROUB BY CUBE(Time, Region)

Grouping (Cont.)

The code result

Time  Region  Profit  1996   East   200,000  1996   All Regions   200,000  All Times   East   200,000  [NULL]   [NULL]   190,000  [NULL]   All Regions   190,000  All Times   [NULL]   190,000  All Times   All Regions   390,000  

Grouping (Cont.)Also we can use GROUPING function for this purpose

we retrieve a subset of the subtotals created by a CUBE and none of the base-level aggregations. The HAVING clause constrains columns which use GROUPING

functions

SELECT Time, Region, Department, SUM(Profit) AS Profit, GROUPING (Time) AS T, GROUPING (Region) AS R, GROUPING (Department) AS D

FROM Sales GROUP BY CUBE (Time, Region, Department) HAVING (D=1 AND R=1 AND T=1)

OR (R=1 AND D=1) OR (T=1 AND D=1)

Grouping (Cont.)

The query result

Time  Region  Department  Profit  1996   [NULL]   [NULL]   526,000  1997   [NULL]   [NULL]   598,000  [NULL]   Central   [NULL]   316,000  [NULL]   East   [NULL]   442,000  [NULL]   West   [NULL]   366,000  [NULL]   [NULL]   [NULL]   1,124,000  

Roll up Example

SELECT Year, Quarter, Month, SUM(Profit) AS Profit

FROM sales GROUP BY ROLLUP(Year, Quarter, Month)

Year  Quarter  Month  Profit 1997   Winter   Jan   55,000  1997   Winter   Feb   64,000  1997   Winter   March   71,000  1997   Winter   [NULL]   190,000  1997   Spring   April   75,000  1997   Spring   May   86,000  1997   Spring   June   88,000  1997   Spring   [NULL]   249,000  1997   Summer   July   91,000  1997   Summer   August   87,000  1997   Summer   September   101,000  1997   Summer   [NULL]   279,000  1997   Fall   October   109,000  1997   Fall   November   114,000  1997   Fall   December   133,000  1997   Fall   [NULL]   356,000  1997   [NULL]   [NULL]   1,074,000  

The query result

Roll up Example

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