Creation of Infoobjects

Creation of Infoobject Chars and Keyfigures

Extracts

Extracts:

Since internal tables have fixed line structures, they are not suited to handle data sets with varying structures. Instead, you can use extract datasets for this purpose.

An extract is a sequential dataset in the memory area of the program. You can only address the entries in the dataset within a special loop. The index or key access permitted with internal tables is not allowed. You may only create one extract in any ABAP program. The size of an extract dataset is, in principle, unlimited. Extracts larger than 500KB are stored in operating system files. The practical size of an extract is up to 2GB, as long as there is enough space in the filesystem.

An extract dataset consists of a sequence of records of a pre-defined structure. However, the structure need not be identical for all records. In one extract dataset, you can store records of different length and structure one after the other. You need not create an individual dataset for each different structure you want to store. This fact reduces the maintenance effort considerably.

In contrast to internal tables, the system partly compresses extract datasets when storing them. This reduces the storage space required. In addition, you need not specify the structure of an extract dataset at the beginning of the program, but you can determine it dynamically during the flow of the program.

You can use control level processing with extracts just as you can with internal tables. The internal administration for extract datasets is optimized so that it is quicker to use an extract for control level processing than an internal table.

Procedure for creating an extract:

1. Define the record types that you want to use in your extract by declaring them as field groups. The structure is defined by including fields in each field group. ----- Defining an Extract -----
2. Fill the extract line by line by extracting the required data.------ Filling an Extract with Data -----
3. Once you have filled the extract, you can sort it and process it in a loop. At this stage, you can no longer change the contents of the extract. ------ Processing Extracts ------

Dimensions

Dimensions:

Fact table and the relevant dimension tables of an InfoCube are connected with one another relationally using the dimension keys. The dimension key is provided by the system per characteristic combination in a dimension table.

• With the execution of a query the OLAP processor checks the dimension tables of the InfoCube to be evaluated for the characteristic combinations required in the selection.
• The dimension keys determined in this way point the way to the information in the fact table.
• Dimension tables consist of a maximum of 248 characteristics.
• The Time dimension holds the time characteristics needed for analysis.
• The Unit dimension contains the unit of measure and currency characteristics needed to describe the key figures properly.

The Data Packet dimension is used to identify discrete packets of information loaded into the InfoCube. In this way, packets can be deleted, reloaded or maintained individually

InfoCubes are made up of a number of InfoObjects. All InfoObjects (characteristics and key figures) are available InfoCube-independently. Characteristics relate to master data with their attributes and text descriptions.

An InfoCube consists of several InfoObjects and is structured according to the star schema. This means there is a (large) fact table that contains the key figures for the InfoCube as well as several (smaller) dimension tables which surround it. The characteristics of the InfoCube are stored in these dimensions.

An InfoCube fact table only contains key figures, in contrast to an ODS object, whose data part can also contain characteristics. The characteristics of an InfoCube are stored in its dimensions.

The dimensions and the fact table are linked to one another via abstract identification numbers (dimension IDs), which are in the key part of the particular database table. As a result, the key figures of the InfoCube relate to the characteristics of the dimension. The characteristics determine the granularity (the degree of fineness), in which the key figures are managed in the InfoCube.

Characteristics that logically belong together (district and area, for example, belong to the regional dimension) are grouped together in a dimension. By adhering to this design criterion, dimensions are to a large extent independent of each other, and dimension tables remain small with regards to data volume, which is desirable for reasons of performance. This InfoCube structure is optimized for Reporting.