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Oracle® Business Intelligence Concepts Guide
10g Release 2 (10.1.2.0.0) Part No. B13970-01 |
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Oracle® Business Intelligence Concepts Guide
10g Release 2 (10.1.2.0.0) Part No. B13970-01 |
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Previous |
Next |
Oracle provides a complete and integrated set of tools to support business intelligence. This chapter describes these tools and how you can use them to create a business intelligence solution for your enterprise.
This chapter contains the following topics:
In the Information Age, corporations have at their disposal massive amounts of data collected in transactional systems. These systems are designed for the efficient selection, storage, and retrieval of data, and are essential for businesses to keep track of their affairs.
Having data is not the same as having information. The challenge is in deriving answers to business questions from the available data. This wealth of data can yield critical information about a business, so that decision makers at all levels can respond quickly to changes in the business climate.
Aggregating data into levels at which patterns can emerge, ordering levels into hierarchies to support drilling down and up through the levels, and using analytic functions such as lag, moving total, and year-to-date are among the techniques used to transform data into information. This information -- commonly called business intelligence -- can provide a significant edge in an increasingly competitive marketplace.
Business intelligence provides answers to basic questions such as:
"What are my top five products?"
"How do my sales this year compare to sales last year?"
"What is the 3-month moving average of my sales?"
Business intelligence can also answer more probing analytical questions such as:
Why are sales down in this region?
What can we predict for sales next quarter?
What factors can we alter to improve the sales forecast?
How will our margins improve if we run this promotion?
Answering these questions requires an analysis of past performance, so that key decision makers can set a course for their businesses that will improve future performance, provide a more competitive edge, and thus enhance profitability. The Oracle Business Intelligence solution provides the information needed by decision makers whose ability to set goals today is dependent on how well they can predict the future.
Getting the answers to these questions involves:
Access to summary historical and current data
Calculations on the data
Time-series analysis
Forecasting
Modeling
What-if analysis
The technology to perform these computations and to present the results of analysis is contained in Oracle Business Intelligence.
Oracle Business Intelligence enables you to shape both long-term goals and day-to-day decisions, providing you with time-critical, relevant, and accurate information.
Oracle provides a complete and integrated business intelligence platform that meets these basic business requirements:
Integrated query and analysis. Regardless of technical expertise, users at all levels of an organization need to examine slices of data that are pertinent to their decisions, and to delve into that data as necessary in pursuit of business insights. Oracle provides query and analysis tools that enable users to customize their views of the data, to drill down to examine contributing factors, and to drill up to see how these factors contribute to the whole. Moreover, users can customize reports to suit their individual needs for visualizing the data.
Collaboration. In a large organization, the ability to share reports has paramount importance. On a business level, it enables users who are working together to share their insights. On a technical level, it avoids duplication of effort and thus a waste of human resources.
Design and life-cycle management. With graphical user interfaces, users can design a data store that supports the types of analysis required for making business decisions, load data from a wide variety of data sources, and manage that data store throughout its life span.
Security, manageability, and scalability. All of the data for business intelligence is stored in Oracle Database as a single source of truth. It is not distributed in hundreds of spreadsheets, nor is it stored in a separate multidimensional database. All of the safety and security of Oracle Database applies to the business data, regardless of whether it is relational or multidimensional. Moreover, your technical staff uses the same tools to allocate resources, monitor performance, and so forth, so additional training is minimized.
Oracle offers tools for users involved at all stages in the life cycle of business intelligence:
Information Consumers
Report Developers and Analysts
Database Administrators
Applications Developers
This chapter describes the components that comprise Oracle Business Intelligence for each of these user groups.
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Note: If you do not have the components that you need to implement your Oracle business intelligence solution, then consult the Oracle Technology Network Web site athttp://www.oracle.com/technology. You can view a wealth of information and download products, documentation, and more.
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Oracle Business Intelligence provides online analytical processing (OLAP) in addition to its relational analytic and reporting capabilities. OLAP functionality is characterized by dynamic, multidimensional analysis of historical data, which supports activities such as the following:
Calculating across dimensions and through hierarchies
Analyzing trends
Drilling up and down through hierarchies
Rotating to change the dimensional orientation
Forecasting
What-if analysis
Oracle Database Enterprise Edition with the OLAP option provides multidimensional technology to support business intelligence. The OLAP option is fully integrated into Oracle Database. With this integration, Oracle provides the power of multidimensional analysis along with the manageability, scalability, and reliability of Oracle Database.
Most users are interested in getting insight into their business data in order to make decisions. They gain these insights by consuming information that is presented in reports. They need to identify the trends and patterns in their data in order to plan their business strategies, but they want to spend as little time as possible on the technical aspects of designing and developing reports. Using Oracle's Web-based solution, they can use their browsers to access their data -- which means no software installation, configuration, or maintenance for the majority of business intelligence users.
These integrated tools make this possible:
Users can view predefined reports in a dashboard or drill down on a portlet into Discoverer Viewer, where they can modify these reports for their particular needs. The data storage format is transparent, so that multidimensional and relational data stores are indistinguishable.
Discoverer Portlet Provider in conjunction with OracleAS Portal simplifies the creation of dashboards containing reports developed in Discoverer. Report builders can take advantage of OracleAS Portal's powerful distribution capabilities. A simple wizard lets them publish any existing Discoverer report, making it easy to create secure, personalized dashboards for tracking business performance measures.
These dashboards provide information to Discoverer Viewer. Clicking on the Analyze link in any Discoverer report automatically opens Discoverer Viewer, which is designed specifically with business users in mind. Its easy-to-use, pure-HTML user interface gives convenient access to reports from a standard Web browser, without requiring the installation of any additional desktop software.
Figure 1-1 shows a sample dashboard.
Discoverer Portlet Provider supports the following data models:
Multidimensional (analytic workspaces)
Relational (tables and views)
Discoverer Viewer gives business users immediate access to information published in a dashboard on the Internet or an intranet. For most business users, this is the easiest way to start using Discoverer. From the dashboard, they can drill down to Discoverer Viewer to perform further analysis.
Discoverer Viewer's interactive reporting environment lends itself to self-service business intelligence. Users can drill on crosstabs and graphs to view and analyze the underlying data, thereby identifying trends and anomalies in their business. They can easily tailor the information to their needs, such as changing the graph type used in a report, drilling down into the data, viewing a different selection of data, and adding stoplight formatting to spot trends. They can also customize reports that contain parameters, such as changing the selection of City in a report that shows the top 5 products for a particular city (such as New York). Discoverer enables them to focus on solving business problems and provides them with insight into their data.
Users can also share the results of their analysis by exporting their reports in Excel, HTML, PDF, or other popular file formats, and sending these files as e-mail attachments from within Discoverer Viewer.
Figure 1-2 shows a crosstab from the dashboard in Figure 1-1, which is now displayed in Discoverer Viewer.
Figure 1-2 Discoverer Viewer Displays Reports With User-Settable Parameters
Discoverer Viewer supports the following data models:
Multidimensional (analytic workspaces)
Relational (tables and views)
Report developers, analysts, and other technically advanced users want to design their own reports based on their own ad-hoc analysis of the data. They can then publish their reports for general use.
Oracle offers two query and analysis tools:
Both tools work with multidimensional data stores. Discoverer Plus also works with relational data stores.
Discoverer Plus is an ad-hoc query, reporting, analysis, and Web-publishing tool. It enables more technically advanced users to create new reports for their own analytic pursuits, and then to publish those reports in a dashboard for less technical users to explore in Discoverer Viewer or in an OracleAS dashboard by using Discover Portlet Provider.
Discoverer Plus's intuitive user interface guides users through the entire process of building and publishing sophisticated reports containing crosstabs and graphs. These users can choose from multiple graph types and layout options to create a visual representation of their query results. Using Discoverer Plus, they can create queries, drill, pivot, slice and dice data, add analytic calculations, graph the data, share results with users, and export their Discoverer reports in various data formats.
Discoverer Plus operates against two different types of data store:
Multidimensional. Discoverer uses OLAP metadata to access data in a multidimensional data store. When operating against a multidimensional data store, Discoverer Plus is called Discoverer Plus OLAP. Additional information is provided in "Using Discoverer with Multidimensional Data Stores".
Relational. Discoverer uses an End User Layer (EUL) to access relational data in any format. When operating against an EUL, Discoverer Plus is called Discoverer Plus Relational. Additional information is provided in "Using Discoverer with Relational Data Stores".
Using Discoverer Plus OLAP, users can access and analyze multidimensional data from their company's database, without having to understand complex database concepts. With wizards and menus, Discoverer Plus OLAP guides them through the steps to retrieve and analyze data that supports their business decisions. The OLAP Query Wizard and the OLAP Calculation Wizard are integral components of Discoverer Plus OLAP, as described in this topic. All Oracle Business Intelligence tools that are enabled for OLAP use these wizards.
Using Discoverer Plus OLAP, analysts can formulate their queries in the language of business. Consider the following request for information:
For fiscal years 2003 and 2004, show the percent change in sales for the top 10 products for each of the top 10 customers based on sales.
Sales is a business measure, calculated in US dollars or another currency. Fiscal years, products, and customers are the dimensions that provide a context for the data. This request is articulated in business terms, but easily translates into a query in the language of multidimensional analysis, such as measure and dimension.
The multidimensional data model also facilitates the creation of custom measures. From stored measures, users can select from numerous operators and functions to generate a wealth of information, such as year-to-date, percent change from a year ago, rank, share, and variance calculations.
Most standard queries involve simple data selection and retrieval. However, OLAP queries are more structured and involve calculations, time series analysis, and access to aggregated historical and current data. In OLAP queries, users know the dimensions and hierarchies that structure the measures. Data access is fast; all data operations are measured in seconds, including those with complex calculations.
Figure 1-3 shows a sample report generated by Discoverer Plus OLAP.
Figure 1-3 Sample Crosstab and Bar Graph in Discoverer Plus OLAP
Analysts that have Oracle Database Standard Edition, or Enterprise Edition without the OLAP option, can also use Oracle's business intelligence solution. Discoverer Plus Relational provides efficient interactive report layout and formatting capabilities, and it helps users achieve business insights through value-added analysis. They can add totals and percentages to their reports and add data-driven "stoplight" formatting to identify exceptions. They can also add sophisticated numerical and statistical analysis by leveraging the computational power of Oracle Database.
Figure 1-4 shows a report generated by Discoverer Plus Relational.
Figure 1-4 Sample Table and Pie Graph in Discoverer Plus Relational
Discoverer Plus supports the following data models:
Multidimensional (analytic workspaces)
Relational (tables and views)
OracleBI Spreadsheet Add-In enables analysts to work with live multidimensional data in the familiar spreadsheet environment of Microsoft Excel. The add-in fetches data through an active connection to a multidimensional data store in Oracle Database and displays the data in a spreadsheet. Analysts can use the add-in to perform OLAP operations such as drilling, rotation, and data selection.
OracleBI Spreadsheet Add-In also solves the problem of each spreadsheet user's having personal calculations. Instead, calculations can be defined in the database by the DBA or system administrator and shared across user communities. OLAP calculations are performed quickly and efficiently in the database, and they do not require massive downloads of data to Excel.
Figure 1-5 shows Oracle data in an Excel spreadsheet. Notice the addition of the OracleBI menu to the menu bar.
Figure 1-5 Oracle Data Displayed in an Excel Spreadsheet
Using a wizard-driven interface, users can select data simply by choosing from lists of dimension values. Or they can use various conditions for their selections. In addition, they can create custom measures by using a wizard. The Spreadsheet Add-In uses the same Query Wizard and Calculation Wizard as Discoverer Plus OLAP.
Analysts can also treat Oracle data like regular spreadsheet data. For example, they can create formulas and graphs in Excel, thereby combining the powerful analytic capabilities of Oracle OLAP with standard Excel calculations and formatting.
OracleBI Spreadsheet Add-In supports the following data model:
Multidimensional (analytic workspaces)
Discoverer Plus and Spreadsheet Add-In use the same type of metadata to run against multidimensional data stores, so an instance of Oracle Database can support both tools without incurring additional administrative costs.
To access multidimensional data, which is stored in analytic workspaces, both tools use the Active Catalog. The Active Catalog is a set of relational views that contain OLAP metadata. Discoverer Plus has the additional capability of running against relational tables and views using an EUL.
Figure 1-6 provides an overview of the relationships among the tools, metadata, and data.
Figure 1-6 Access to Data From Query and Analysis Tools
Discoverer Plus and the Spreadsheet Add-In use the same Query Wizard and Calculation Wizard. This replication minimizes the learning curve for analysts using both tools.
The differences between the two tools are primarily in data presentation and report sharing:
Discoverer Plus has been developed primarily for ad-hoc query and analysis. It provides a user interface and wizards that facilitate these activities, such as a navigator that allows in-place modifications to crosstabs, tables, and graphs, and its own presentation formats. It also provides a mechanism for users to manipulate the data display interactively for activities such as drilling up and down through the data.
OracleBI Spreadsheet Add-In populates Excel workbooks, and users can display the data using Excel's graph and report formats. Users can share these spreadsheet files the same way they share other Excel spreadsheets.
Discoverer Plus provides analysts with all of the functionality needed to select, view, and analyze their data. Compared to Spreadsheet Add-In, Discoverer Plus offers more flexibility in swapping dimensions in the layout of reports, formatting, graphing, and defining custom calculations. Analysts can generate crosstabs, tables, and graphs in the same report. Report sharing is facilitated by Oracle Portal and Discoverer Viewer.
However, if analysts at your company are enthusiasts of Excel, then Spreadsheet Add-In provides them with additional analytical capability while building on their expertise in Excel.
DBAs must provide data and metadata suitable for the query and analysis tools that will be used. Discoverer Plus OLAP and Spreadsheet Add-In require multidimensional data stores, as described in the next topic. The following tools are used to create and manage multidimensional data stores:
Discoverer Plus Relational runs against any relational schema. The following tools are used to create and manage relational data stores for Discoverer:
Multidimensional data is stored in analytic workspaces where it can be manipulated by the OLAP engine in Oracle Database. Individual analytic workspaces are stored in tables in a relational schema. Like a relational table, an analytic workspace is owned by a particular user ID, and other users can be granted access to it. Within a single database, many analytic workspaces can be created and shared among users.
Analytic workspaces have been designed explicitly to handle multidimensionality in their physical data storage and manipulation of data. The multidimensional technology that underlies analytic workspaces is based on an indexed multidimensional array model, which provides direct cell access. This intrinsic multidimensionality affords analytic workspaces much of their speed and power in performing multidimensional analysis.
Creating an analytic workspace involves a physical transformation of the data. The first step in that transformation is defining multidimensional objects such as measures, dimensions, levels, hierarchies, and attributes. The next step is mapping the multidimensional objects to the data sources. Last, the data loading process transforms the data from a table format into a multidimensional format.
Analytic workspaces have several different types of data containers, such as dimensions, variables, and relations. Each type of container can be used in a variety of ways to store different types of information, including business measures and metadata.
The analytic workspaces that are created by Oracle Enterprise Manager and Analytic Workspace Manager are in database standard form (typically called simply "standard form"). Standard form specifies the types of physical objects that are used to instantiate logical objects (such as dimensions and measures), and the type and form of the metadata that describes these logical objects. This metadata is exposed to SQL in the Active Catalog. These views are maintained automatically, so that a change to a standard form analytic workspace is reflected immediately by a change to the Active Catalog. Discoverer Plus OLAP and Spreadsheet Add-In use the Active Catalog to query data in analytic workspaces.
An analytic workspace initially contains only detail data loaded from its data sources. Summary data is calculated in the analytic workspace, and the aggregates are stored in the same analytic workspace objects as the base data. The data is always presented to the application as fully solved; that is, both detail and summary values are provided, without requiring that calculations be specified in the query.
Analytic workspaces store aggregate data in an extremely compact form and provide a fast response time. Aggregates can be stored permanently in the analytic workspace, or only for the duration of an individual session, or only for a single query. Aggregation rules identify which aggregates are stored, and which aggregates are calculated on the fly. When an application queries the analytic workspace, either the aggregate values have already been calculated and can simply be retrieved, or they can be calculated on the fly from a small number of stored aggregates. Analytic workspaces are optimized for multidimensional calculations, making these run-time summarizations extremely fast.
Analytic workspaces provide an extensive list of aggregation methods, including weighted, hierarchical, and weighted hierarchical methods.
For most DBAs, relational tables and SQL provide a familiar environment. On the other hand, analytic workspaces require new tools and data transformations. So why use analytic workspaces? The answer is simply that analytic workspaces work better in some situations, and relational schemas work better in others.
This topic discusses the factors that determine which type of data store is the best choice for different kinds of data and analytical requirements. Your requirements may not perfectly match one type of data store, so you will need to weigh the relative importance of each requirement. You should also consider the direction your enterprise is heading in its business intelligence requirements. For example, you should consider whether ad-hoc reporting and advanced analytics are growing in popularity in your organization, or whether the analytic requirements are stable and predictable for the foreseeable future.
Analytic workspaces are the best choice for meeting these user requirements:
Advanced calculations, such as growth ratios and trends
Unpredictable ad-hoc querying of all areas of the data as a routine use of the data store
Custom measures as a routine method of data analysis
What-if scenarios
Relational schemas provide the most satisfactory solution for these user requirements:
Predefined reports, which access predictable areas of the data, as the primary use of the data store
Flexible relationships
Many-to-many parent-child relationships
Attribute hierarchies
The following topics explore the technical differences between multidimensional and relational data stores that support these guidelines.
The multidimensional data model is highly structured. Structure implies rules that govern the relationships among the data and control how the data can be queried. These rules simplify the construction of queries, and this simplicity supports ad-hoc querying of the data.
Analytic workspaces are the physical implementation of the multidimensional model, and thus are highly optimized for multidimensional queries. The OLAP engine leverages the model in performing highly efficient inter-row calculations, time series analysis, and indexing.
Relational schemas can have much less structure, and the relationships among tables and views can be established on a query-by-query basis. This flexibility can be very useful in selecting and reporting the data in existing schemas. Star schemas are frequently used for data warehouses, because they impose a structure that enables the relational engine to optimize analytical queries.
The decision to use analytic workspaces or a relational schema determines how analytic queries are processed.
For data stored in analytic workspaces, the OLAP calculation engine performs analytic operations and supports sophisticated analysis, such as modeling and what-if analysis. If analysts require these types of analyses, then they need analytic workspaces. The OLAP engine also provides the fastest run-time response to analytic queries, which is important if you anticipate user sessions that are heavily analytical.
For data stored in a relational schema, analytical operations are performed by SQL. The MODEL clause and the analytical functions (such as RANK, LEAD, and LAG) enable analysts to include informative calculations in their reports such as year-to-date totals and moving averages.
A basic characteristic of business analysis is hierarchically structured data; detail data is summarized at various levels, which allows trends and patterns to emerge. After an analyst has detected a pattern, he or she can drill down to lower levels to identify the factors that contributed to this pattern.
The creation and maintenance of summary data is a serious issue for DBAs. If no summary data is stored, then all summarizations must be performed in response to individual queries. This can easily result in unacceptably slow response time. At the other extreme, if all summary data is stored, then the database can quickly multiply in size.
The choice of a data store determines how summary data is generated.
Analytic workspaces store aggregate data in the same objects as the base level data. The aggregation subsystem of the OLAP engine precalculates some areas of the cube and calculates other areas on the fly as described in "About Multidimensional Data Stores". Analytic Workspace Manager provides property sheets for defining the rules for generating aggregate data.
Relational schemas store aggregate data in materialized views and summary tables, which are generated by Automated Summary Management (ASM) in Discoverer Administrator.
When predefined reports are run on a routine basis, the DBA knows which areas of the data are queried and what summary data is needed. This situation may be handled easily with a relational schema and a relatively small number of materialized views. However, extensive use of ad-hoc queries and user-defined custom measures create a random situation in which any part of the data store may be queried and summarized. Materialized views may not be available for these queries, so summary data must be generated at runtime.
Analytic workspaces store the summary data for all parts of a cube. Thus, analytic workspaces are a better choice for installations where users perform extensive ad-hoc querying and frequently define custom measures.
Oracle Warehouse Builder enables the extraction, transformation, and loading of heterogeneous data to produce quality information. It is the only enterprise business intelligence design tool that manages the full life cycle of data and metadata integration for Oracle Database 10g.
Among the main components in any business intelligence solution are the source systems that the business intelligence system will report upon. Warehouse Builder provides an easy-to-use, wizard-driven interface with which to capture the metadata for the source systems. DBAs can then use the metadata representations of the source to model the extraction processes.
After capturing the metadata for the sources and designing the target schema, DBAs can create the data flows that define how data moves from sources to targets. They can map multiple sources into multiple targets, specify chained transformations, and apply complex PL/SQL transformations to the data. The mapping component also enables them to perform common operations such as joining, filtering, aggregating, and ordering data, as well as advanced and set-based operations. Warehouse Builder provides a graphical environment to model the ETL processes.
Figure 1-7 shows the Mapping Editor canvas, which defines how the target Sales table will be populated with the joined and transformed data from two source tables.
Warehouse Builder can build a fully functional data warehouse, including analytic workspaces. Warehouse Builder 10.1.0.2.0 generates analytic workspaces in Oracle9i standard form. Analytic Workspace Manager must be used to upgrade analytic workspaces to Oracle10g standard form.
Warehouse Builder can also be used to build a relational schema and Discoverer End User Layer (EUL) for Discoverer Plus Relational.
Warehouse Builder generates lineage reports and impact analysis reports, which help manage the build process over the life cycle of the database.
Lineage reports answer the question, Where did my data come from?
Impact analysis reports answer the question, What is the impact of a particular change in the source tables or files?
These reports are produced in HTML.
Analytic Workspace Manager is a primary tool for developing and managing analytic workspaces. DBAs and application developers can use it to define a logical multidimensional model, generate summary data, add derived measures, and refresh the base data from its relational sources in Oracle Database.
The main window in Analytic Workspace Manager provides access to two views:
Model View. This view is used to define a logical multidimensional model of the data and to instantiate that model in a standard form analytic workspace. A drag-and-drop user interface facilitates mapping of the logical objects to columns in relational tables or views in Oracle Database. The source data can be in a star, snowflake, or any other type of schema that provides the level and attribute relationships required for OLAP analysis.
All summarization and manipulation of the data is performed within the analytic workspace. The Model View is also used to generate summary data using a variety of operators. "Summary Data" provides additional information about summarization.
DBAs can also define calculated measures by using the same Calculation Wizard that appears in Discoverer Plus OLAP and Spreadsheet Add-In.
Object View. This view is for advanced administrators who want to examine, modify, or create physical object in an analytic workspace.
Figure 1-8 shows the definition of a Product dimension in the Model View.
Star, snowflake, or other relational schemas that contain dimension level and attribute relationships
Updates and new releases of Analytic Workspace Manager are distributed as they become available on http://www.oracle.com/technology/products/bi/olap/olap.html
Discoverer Administrator is a powerful management tool for overseeing the database end of a relational Discoverer solution. With it, DBAs can secure the reporting environment through user privileges and access rights. Discoverer Administrator takes advantage of native database security or Oracle Applications security, so DBAs never have to define the users or their access privileges twice. All the existing database or Oracle Applications security policies are adhered to automatically.
Discoverer Administrator also manages the Discoverer reporting environment. Using this tool, DBAs can create Discoverer End User Layers (EULs) and Business Areas. By tuning the EUL (a semantic layer that conceals database names, joins, and other complexities), they can transform the database into an easy-to-understand report authoring environment for non-technical users, giving them access to the data they need without further DBA involvement.
DBAs can use Discoverer Administrator to make any modifications to an EUL that was created using Oracle Warehouse Builder.
Discoverer Administrator supports the following data model:
Any relational schema
Warehouse Builder is a tool for Information Technology (IT) professionals who manage production systems. It is a powerful tool that can generate analytic workspaces as one element in a larger ETL process. It can also generate EULs along with relational data sources.
Analytic Workspace Manager is an easy-to-use tool designed for application developers, departmental DBAs, and other nonprofessional DBAs. It enables them to design and develop a data model quickly and interactively based on their reporting needs. After the data model has been developed and its design is stable, the IT department may assume responsibility for generating the analytic workspace using Warehouse Builder. Analytic Workspace Manager can be used to embellish the analytic workspaces created by the IT department, such as by adding custom measures.
Discoverer Administrator serves the same function for relational data stores that Analytic Workspace Manager serves for multidimensional data stores.
Application developers can create custom Java business intelligence applications and targeted solutions by using Oracle Business Intelligence Beans (BI Beans).
BI Beans is a set of standards-based Java beans. It provides analysis-aware application building blocks designed for Oracle Database. Using BI Beans, developers can build business intelligence applications that take advantage of the robust analytic capabilities available in Oracle Database, including Oracle OLAP. Applications can include advanced features such as interactive user interfaces, drill-to-detail reports, forecasting, and what-if analysis. Figure 1-9 shows a custom BI Beans application that presents two graphs:
Bubble graph for margin analysis, in which the bubble size indicates sales revenue
Line graph for forecasts, in which each line represents a different forecast method
BI Beans includes Java beans for acquiring the data from Oracle Database (including the Query Wizard and Calculation Wizard described in "Tools for Report Developers and Analysts"), presenting the data in a wide variety of crosstab and graph formats, and saving report definitions, custom measures, data selections, and so forth.
BI Beans is seamlessly integrated into Oracle JDeveloper, providing a productive development environment for building custom business intelligence applications. Using JDeveloper and BI Beans, application developers can build Internet applications quickly and easily.
Data Requirements for BI Beans
BI Beans supports the following data model:
Multidimensional (analytic workspaces)
Distribution
BI Beans is distributed on:
Oracle Developer Suite 10g CD-ROM Pack: Oracle Business Intelligence Tools 10g Release 2 Version 10.1.2.0.0 CD-ROM
First install Oracle JDeveloper 10g (9.0.5).
See Also
BI Beans Help from within JDeveloper
