Improving Expressive Power in Modeling Data Warehouse and OLAP Applications

Improving Expressive Power in Modeling Data Warehouse and OLAP Applications

Elzbieta Malinowski (University of Costa Rica, Costa Rica)
DOI: 10.4018/978-1-60566-816-1.ch002
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Data warehouse and OLAP systems are widely required during the decision-support process, since they provide integrated data in a form that facilitates the expression of complex queries. In order to exploit both systems to their full capabilities, dimensions with hierarchies must be clearly defined. Dimensions can be of different types and they allow users to see quantified data from different perspectives. Hierarchies are important in analytical applications, since they give users the possibility of representing data at different abstraction levels. However, even though there are different kinds of hierarchies in real-world applications and some of them are already implemented in commercial tools, there is still a lack of a well-accepted conceptual model that allows decision-making users to express their analysis needs. In this chapter, we show how the conceptual multidimensional model can be used to facilitate the representation of complex hierarchies and different kinds of dimensions in comparison to their representation in the relational model and commercial OLAP tools, using as an example Microsoft Analysis Services.
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A Data Warehouse (DW) provides users with high quality data organized in a way that facilitates expression of complex queries, ensuring at the same time efficient and accurate responses to such queries. Different systems and tools, such as online analytical processing (OLAP) systems, can be used to access and analyze the data contained in DWs. These systems allow users to interactively query and automatically aggregate data using roll-up and drill-down operations. The former operation transforms detailed data into a summarized one, e.g., daily sales into monthly sales, while the latter operation does the contrary.

The data for DW and OLAP systems is usually organized into fact tables related to several dimension tables. A fact table (Sales in Figure 1) represents the focus of analysis (e.g., analysis of employees’ sales) and typically includes attributes called measures. These are usually numeric values (e.g., Quantity and Amount in Figure 1) that facilitate a quantitative evaluation of various aspects of interest. Dimensions (e.g., Sales territory in Figure 1) are used to see the measures from different perspectives, e.g., according to geographic distribution of a company. Dimensions typically include attributes that form hierarchies. When a hierarchy is traversed from finer to coarser levels, measures are aggregated, e.g., moving in a hierarchy from a product to a subcategory will give aggregated values of sales for different products subcategories.

Figure 1.

Example of a DW for analyzing employees’ sales

Hierarchies can be included in a flat table (e.g., attributes City-County-State in the Employee table in Figure 1) forming the so-called star schema or using a normalized structure (e.g., tables Product, SubCategory, and Category in the figure), called the snowflake schema.

In order to exploit OLAP systems to their fullest capabilities hierarchies must be clearly defined. Hierarchies are important in analytical applications, since they represent data at different abstraction levels. However, in real-world situations, users must deal with different kinds of hierarchies that either cannot be represented using the current DW and OLAP systems or are represented at the logical level without the possibility of capturing the essential semantics of multidimensional applications. For example, the Employee table includes a hierarchy that represents the supervisor-supervisee relationship (the attributes Employee key and Supervisor key); this hierarchy is difficult to distinguish even though it may be important to consider during the analysis process. Another hierarchy in the same table can be formed by the City, County, and State attributes. However, the schema in Figure 1 does not reflect the real situation about the geographical division of a country where some states do not divide themselves in counties. In addition, the Sales territory table includes a hierarchy composed of Sales territory region, Sales territory country, and Sales territory group; nevertheless, the schema in Figure 1 does not represent the hierarchy clearly since some countries are not partitioned into regions, i.e., this attribute will have a null value for those countries that do not have regions. .

A similar situation has occurred with different kinds of dimensions. They have been proposed mainly by practitioners and implemented in some commercial tools due to a growing interest in having DW and OLAP systems in different application areas. Nevertheless, these different kinds of dimensions are difficult to represent using models of current DW and OLAP systems and the proposed implementation solutions are not clear for non-experienced users. An example is the so-called role-playing dimension (the Time table in Figure 1) which participates in the fact table several times playing different roles, i.e., in the figure indicating a date of requesting a product and paying for it.

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