Introducing the BI Beans

The BI Beans provide reusable components that are the basic building blocks for OLAP decision support applications. Using the BI Beans, developers can rapidly develop and deploy new applications, because these large functional units have already been developed and tested -- not only for their robustness, but also for their ease of use. And because the BI Beans provide a common look and feel to OLAP applications, the learning curve for end users is greatly reduced.

Two groups of BI Beans are currently available:

• Presentation Beans display the data in a rich variety of formats so that trends and variations can easily be detected. Among the Presentation BI Beans currently available are Graph, Table, and Crosstabs.

  The Presentation Beans can be implemented as a thick client or a thin client. Thick clients best support users who do immersed analyses, that is, use the system for extensive periods of time with a lot of interaction. For example, users who create reports benefit from a thick client. Thin clients best support remote users who use a low bandwidth connection and have basic analytical needs. Thin clients can be embedded in a portal or other Web site for these users.

• OLAP BI Beans acquire and manipulate the data. The OLAP BI Beans use the OLAP API to connect to a data source, define a query, manipulate the resultant data set, and return the results to the Presentation BI Beans for display.

You can use the BI Beans in either thick-client or thin-client applications.

Thick-Client Configuration

The components of an OLAP thick-client application are grouped into three tiers, which can be on separate platforms or the same platform:

• Java client tier. A Java application can run either in a browser or directly in the Java Runtime Environment (JRE). The BI Beans that are dedicated to presenting the data and metadata also run on this tier.
• Application server tier. The "brains" of the application run on this tier, which includes the OLAP API and the OLAP BI Beans that are built using the OLAP API.
• Data server tier. The Oracle RDBMS and OLAP service form the data server tier, where the data is stored, selected, and manipulated. An OLAP API component also runs on the data server tier.

Figure 3-2 shows these relationships in a thick-client configuration.

Figure 3-2 Thick Client Configuration

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Thin-Client Configuration

The components of an OLAP thin-client application are grouped into two tiers, which can be on separate platforms or the same platform:

• Application server tier. The "brains" of the application run on this tier, which includes a Web server, the OLAP API and the OLAP BI Beans (both presentation and analytical).
• Data server tier. The Oracle RDBMS is the data server tier, where the data is stored, selected, and manipulated either in relational tables or in the OLAP analytic workspace. An OLAP API component also runs on the data server tier.

Figure 3-3 shows these relationships in a thin-client configuration.

Figure 3-3 Thin-Client Configuration

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Metadata

The OLAP API and the BI Beans use the OLAP Catalog to provide the information they need about multidimensional objects defined in an Oracle data warehouse, such as measures and dimensions. For information about metadata and other requirements, refer to Chapter 4, "Designing Your Database for OLAP".

Runtime Repository

The BI Beans employ a runtime repository in the Oracle database that allows users to save their personal analyses and to share their discoveries with other users.

Navigation

The Presentation BI Beans support navigation techniques such as drilling, pivoting, and paging.

• Drilling displays lower-level values that contribute to a higher-level aggregate, such as the cities that contribute to a state total.
• Pivoting rotates the data cube so that the dimension members that appeared along the X-axis of a graph now appear along the Y-axis, or the dimension members that labeled columns in a crosstab now label rows instead. For example, if products label the rows and regions label the columns, then you can pivot the data cube so that products label the columns and regions label the rows.
• Paging handles additional dimensions by showing each member in a separate graph, crosstab, or table rather than nesting them in the columns or rows. For example, you might want to see each time period in a separate graph rather than all time periods on the same graph.

Formatting

The Presentation BI Beans allow you to change the appearance of a particular display. In addition, the values of the data itself can affect the format.

• Number formatting. Numerical displays can be modified by changing their scale, number of decimal digits and leading zeros, currency symbol, negative notation, and so forth. Currency symbols and scaling factors can be displayed in the column or row headers rather than in the cells.
• Stoplight formatting. The formatting of the cell background color, border, font, and so forth can be data driven so that outstanding or problematic results stand out visually from the other data values.
• Ranking. In ranking reports, the numerical rank of each dimension value, based on the value of the measure, is displayed.

Graphs

The Graph bean presents data in a large selection of two- and three-dimensional business chart types, such as bar, area, line, pie, ring, scatter, bubble, pyramid, and stock market. Many of the 2D graphs can be displayed as clustered, stacked, dual-Y, percentage, horizontal, vertical, or 3D effect.

Bar, line, and area graphs can be combined so that individual rows in the data cube can be specified as one of these graph types. You can also assign marker shape and type, data line type, color, and width, and fill colors on a row-by-row basis.

The graph image can be copied to the system clipboard and exported in GIF and other image formats.

Users can zoom in and out of selected areas of a graph. They can also scroll across the axes.

Crosstabs

The Crosstab bean presents data in a two-dimensional grid similar to a spreadsheet. Multiple dimensions can be nested along the rows or columns, and additional dimensions can appear as separate pages. Among the available customizations are: Font style, size, color and underlining; individual cell background colors; border formats; and text alignment.

Users can navigate through the data using either a mouse or the keyboard. They can insert rows and columns to display totals, and edit cells for what-if analysis.

Tables

The Table bean presents data in record format like a relational table or view. In contrast to the crosstab, the table display handles measures individually rather than as members of a measure dimension. Thus, each measure can be manipulated individually.

OLAP BI Beans

The OLAP BI Beans use the OLAP API to provide the basic services needed by an application. They enable clients to identify a database, present credentials for accessing that database, and make a connection. The application can then access the metadata and identify the available data. Users can select the measures they want to see and the specific slice of data that is of interest to them. That data can then be modified and manipulated.

Wizards

The BI Beans offer wizards that can be used both by application developers in creating an initial environment and by end users in customizing applications to suit their particular needs. The wizards lead you step-by-step so that you provide all of the information needed by an application. The following are some of the tasks that can be done using wizards.

• Building a query. Fact tables and materialized views often contain much more data than users are interested in viewing. Fetching vast quantities of data can also degrade performance unnecessarily. In addition to selecting measures, you can limit the amount of data fetched in a query by selecting dimension members from a list or using a set of conditions. A selection can be saved and used again just by picking its name from a list.

  The BI Beans take advantage of all of the new OLAP functions in the database, including ranking, lag, lead, and windowing. End users can create powerful queries that ask sophisticated analytical questions, without knowing SQL at all.

• Generating custom measures. You can define new "custom" measures whose values are calculated from data stored within the database. For example, a user might create a custom measure that shows the percent of change in sales from a year ago. The data in the custom measure would be calculated using the lag method on data in the Sales measure. Because a DBA cannot anticipate and create all of the calculations required by all users, the BI Beans enable users to create their own.