InfoCube Design Alternative II-Dimension Characteristics

In this section, we discuss yet another design alternative. This time, we will create a new IO_SREP, called IO_SREPN2. We will treat IO_SREG and IO_SOFF as independent characteristics, just like IO_SREPN2, and put them all together in the same dimension. Figure 7.3 shows a simplified star schema for this design.



In Figure 7.3, /BIC/SIO_SREG and /BIC/SIO_SOFF have their own master data table and text table. These tables are not shown in the figure.

This design methodology is known as the Dimension Characteristics method. The following steps explain how to build this new design.

Work Instructions

Step 1. Repeat the Steps in Section 2.3 to create IO_SREPN2. It has no hierarchies.





Step 2. Create an InfoCube and include IO_SREG and IO_SOFF as characteristics.





Step 3. Assign IO_SREG, IO_SOFF, and IO_SREPN2 to the same dimension as shown in this screen. (See Screen 2.27 for the difference.)

Click graphics/check.gif to check the new InfoCube. If it is valid, click graphics/activate.gif to activate the new InfoCube.





Step 4. We also need to include IO_SREG and IO_SOFF in the communication structure. (See Screen 3.52 for the difference.)





Step 5. Load data into the new InfoCube and create a query.

In the left panel, we see the three characteristics Sales office, Sales region, and Sales representative. They are all in the same dimension.





Step 6. As before, we specify 31.12.9999 as the key date, and run the query.




Screen 7.28 shows the query result. The Denver office is listed under the Midwest region, instead of the West region, although we specified 31.12.9999 as the key date. This result arises because the sales transactions conducted by the Denver office all took place before January 1, 2000 (see Table 1.4). In the data warehousing world, this query result is referred to as a yesterday-or-today scenario – the data were valid when they were generated.

In a yesterday-and-today scenario, the data that were valid yesterday and today are displayed. In our example, we would not see the Denver office data in a yesterday-and-today scenario. For further information on this scenario, refer to ASAP for BW Accelerator, "Multi-Dimensional Modeling with BW."

Now we know that our new InfoCube design does not provide the two views of data that we saw earlier with the time-dependent hierarchy structure and time-dependent navigational attributes – namely, the today-is-yesterday scenario and the yesterday-is-today scenario.




From a performance point of view, this design improves upon the two earlier options, because it places IO_SREG and IO_SOFF closer to the fact table.

Performance is, of course, one of the major concerns in data warehousing. Here are some guidelines for dealing with this issue:

  1. If IO_SREG and IO_SOFF data are included in the transaction data, as shown in Table 7.1, use IO_SREG and IO_SOFF as dimension characteristics instead of characteristic attributes.

Table 7.1. SALES DATA

CUST001 EAST ATLANTA SREP01 MAT001 2 CS 1 2 19980304
CUST002 EAST NEW YORK SREP02 MAT002 2 CS 2 4 19990526
CUST002 EAST NEW YORK SREP02 MAT003 5 CS 3 15 19990730
CUST003 EAST NEW YORK SREP03 MAT003 5 CS 4 20 20000101
CUST004 MIDWEST DALLAS SREP04 MAT004 50 EA 5 250 19991023
CUST004 MIDWEST DALLAS SREP04 MAT005 100 EA 6 600 19980904
CUST004 MIDWEST DALLAS SREP04 MAT005 100 EA 7 700 19980529
CUST005 MIDWEST CHICAGO SREP05 MAT006 200 EA 8 1600 19991108
CUST006 MIDWEST CHICAGO SREP06 MAT007 20 EA 9 180 20000408
CUST007 MIDWEST CHICAGO SREP07 MAT008 3 DZ 10 30 20000901
CUST007 MIDWEST CHICAGO SREP07 MAT008 3 DZ 1 3 19990424
CUST008 MIDWEST DENVER SREP08 MAT008 3 DZ 2 6 19980328
CUST008 MIDWEST DENVER SREP08 MAT009 2 CS 3 6 19980203
CUST008 MIDWEST DENVER SREP08 MAT010 1 LB 4 4 19991104
CUST009 WEST LOS ANGLES SREP09 MAT011 1.5 LB 5 7.5 20000407
CUST010 WEST SEATTLE SREP10 MAT011 1.5 LB 6 9 20000701
CUST010 WEST SEATTLE SREP10 MAT011 1.5 LB 7 10.5 19990924
CUST010 WEST SEATTLE SREP10 MAT012 2 LB 8 16 19991224
CUST010 WEST SEATTLE SREP10 MAT013 3 CS 9 27 20000308
CUST011 WEST SEATTLE SREP10 MAT014 1 LB 10 10 19980627
CUST012     SREP11 MAT014 2 LB 1 2 19991209
CUST012     SREP11 MAT015 3 CS 2 6 19980221
CUST012     SREP11 MAT015 2 CS 3 6 20000705
CUST012     SREP11 MAT015 3.5 CS 4 14 20001225
  1. If IO_SREG and IO_SOFF are frequently used for navigation, use IO_SREG and IO_SOFF as dimension characteristics instead of characteristic attributes.

When considering the dimension in which a characteristic should be placed, follow these two guidelines:

  1. If the characteristics, such as IO_SREG, IO_SOFF, and IO_SREPN2, have a one-to-many relationship, group them in the same dimension.
  2. If the characteristics, such at IO_MAT and IO_CUST, have a many-to-many relationship, group them in different dimensions. In some special cases, when the combinations of the relations are small, such as materials and colors, you might consider grouping them within one dimension.

Another advantage of this InfoCube design is that we can create aggregates on IO_SREG and IO_SOFF. As in Alternative I, however, the levels of the sales organization are fixed in Alternative II.

Part I. Guided Tours

Business Scenario and SAP BW

Creating an InfoCube

Loading Data into the InfoCube

Checking Data Quality

Creating Queries and Workbooks

Managing User Authorization

Part II. Advanced Topics

InfoCube Design

Aggregates and Multi-Cubes

Operational Data Store (ODS)

Business Content

Generic R/3 Data Extraction

Data Maintenance

Performance Tuning

Object Transport

Appendix A. BW Implementation Methodology

Object Transport

Appendix B. SAP Basis Overview

Object Transport

Appendix C. Glossary

Appendix D. Bibliography

SAP Bw. A Step-By-Step Guide
Sap Bw: a Step By Step Guide for Bw 2.0
Year: 2002
Pages: 106 © 2008-2017.
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