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FIGURE 2-4 InfoObject types
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would be the base characteristic for this hierarchy. If you use this hierarchy in the query, you could define the information more clearly in terms of the location of a specific customer. The properties of a key figure are additional information about the key figures that will help us with the development of the query format and information. A key figure is assigned additional properties that influence the way data is loaded and how the query is displayed. This includes the assignment of a currency or unit of measure, setting aggregation and exception aggregation, and specifying the number of decimal places in the query. We will run into these properties again directly in the Query Designer while creating queries.
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NOTE We will use the Query Designer to create a query definition. This definition can be used in
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the Web Application Designer, BEx Analyzer, Report Designer, or the Workbooks. Now that you have a better understanding of the InfoObject basics, we can discuss the definition(s) of an InfoProvider. InfoProviders are different meta-objects in the data basis that can be seen within a query definition as uniform data providers. They are a combination of characteristics and key figures based on the structure of their data and can be analyzed in a uniform way. The type of data staging and the degree of detail or proximity to the source system in the data flow diagram of a specific company differs from InfoProvider to InfoProvider. However, in the BEx Query Designer, they are seen as uniform objects. Depending on the type of InfoProvider, you will see the InfoObjects that comprise these InfoProviders, organized and mapped differently for the defined requirements and responsibilities of that InfoProvider. We will discuss the unique architecture as we work our way through the details of each object. As you will see, not only do InfoObjects comprise an InfoProvider, but an InfoObject can also be an InfoProvider. This makes sense if we remember that an InfoProvider is an object in the BI system against which a query/report is generated. Therefore, we can use this fact, and the fact that an InfoObject is an InfoProvider, to assume that we can create/run a query against the master data of an InfoObject.
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2:
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Storing the Data SAP/Business Warehouse InfoProviders
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Data Targets
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As mentioned, Data Targets are InfoProviders that hold data. They are used as the final uploading component for the transactional data and offer the ability to generate queries against the data for analysis. The architecture for uploading data from the datasource to the InfoProvider is a discussion for another time, but it is important to understand some of the options you have during this uploading process that will affect your queries, both from a display point of view and a performance point of view. One of the initial questions that needs to be answered for the purposes of both uploading and querying involves the granularity of the data being loaded. As you will see, the granularity of the data will direct you to use a specific InfoProvider either the InfoCube or the DataStore Object (DSO). For example, we need to determine whether we are uploading the data at the material item level or at the material group level In this case very different levels of granularity can be seen number of materials equaling 400,000 versus material groups equaling 5,000. In this situation we would be able to report to different areas of the corporation at the middle management level reports at the material level versus at the C level reports at the material group level. Another question to be answered involves the time element. It is important to collect the data at the correct time level, whether it is months, days, years, quarters, or some other time group. This can have a significant effect on the performance of the data because if you are collecting the data at the monthly level, everything you upload has a level of at least 12 months (versus collecting data by day, for example, which would mean you are collecting the data and multiplying it by 365). As you can see, this would result in a significant difference in data being stored and possibly performance of the query. Another question during the process of setting up the architecture of the uploads involves the uploading process for currency and unit of measure (UOM). In the 7.0 BI version, we can upload the transactional currency or UOM and then have the query execute the currency or unit of measure conversion on the fly, or we can execute the conversion of the UOM or currency during the upload. Your decision depends on the use of the data in the querying process and the performance effort for the query. If you are looking to store the currency in the local version and want to be able to query at either the local level (transactional level) or global level (view based on the corporate level), you would be uploading the currency of the original transaction and setting up the currency translation process in the query. If the only type of reporting you are developing is at the global level (global currency view), the option of executing the conversion during the upload would be more consistent with the data the query is reporting. Therefore, the query performance would not be affected by the requirement to execute the currency conversion at query runtime. A similar questioning process would be used in decisions concerning the unit of measure and whether the upload would generate a consistent unit of measure (based UOM, such as pieces or each or bottles ) or you would use the approach of generating a consistent UOM during the querying process. Another question during the decision of what to upload and in what format is the idea of whether or not to precalculate the data. Precalculation of data refers taking the basic information and, during the upload, summarizing or manipulating it in some way that we have a grouped view of the information. Therefore, rather than uploading all the elements of net profit, based on specific general ledger accounts we can upload the data and, during the upload, calculate the net profit. We can then store this number as one amount rather than as the basic elements, and then during the querying process we can complete the calculation. Depending on the use of each of these elements, we can answer this question. If the identified
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