The aim of this page is to provide a glossary of common OLAP and data warehousing terms and expressions. This list is for all terms beginning with O

Object Type

Oracle OLAP Supports the following types of objects:

• AGGMAP
• COMPOSITE
• DIMENSION
• DIMENSION (simple)
• DIMENSION (conjoint)
• DIMENSION CONCAT
• DIMENSION ALIASOF
• FORMULA
• MODEL
• PARTITION TEMPLATE
• PROGRAM
• RELATION
• SURROGATE
• VALUESET
• VARIABLE
• WORKSHEET

Most of these objects types are hidden from view in AWM since the normal operating mode is to only expose Standard Form obejcts. AWM can be switched to view these object types by changing the View mode to "Object View", and AWM should then look like this:

The type of objects listed above are not exposed at all in Warehouse Builder. Now compare the above image to the Standard Form view of these objects within AWM. This called the "Model View".



Warehouse Builder does support Standard Form objects.

See also AWM, Composute, Cube, Dimension, Standard Form, Variable

OLAP (Online Analytical Processing)

Relational databases provide the online transactional processing (OLTP) that is essential for businesses to keep track of their affairs. Designed for efficient selection, storage, and retrieval of data, relational databases are ideal for housing gigabytes of detailed data. The success of relational databases is apparent in their use to store information about an increasingly wide scope of activities. As a result, they contain a wealth of data that can yield critical information about a business. This information can provide a significant edge in an increasingly competitive marketplace.

A standard transactional query might ask, "When did order 84305 ship?" This query reflects the basic mechanics of doing business. It involves simple data selection and retrieval of one record (or, at most, several related records) identified by a unique order number. Any follow-up questions, such as which postal carrier was used and where was the order shipped to, can probably be answered by the same record. This record has a useful life span in the transactional world: it begins when a customer places the order and ends when the order is shipped and paid for. At this point, the record can be rolled off to an archive.

In contrast, a typical series of analytical queries might ask, "How do sales in the Pacific Rim for this quarter compare with sales a year ago? What can we predict for sales next quarter? What factors can we alter to improve the sales forecast? What happens if I change this number?"

These are not questions about doing business transactions, but about analyzing past performance and making decisions that will improve future performance, provide a more competitive edge, and thus enhance profitability. The analytic database is a "crystal ball" for decision makers whose ability to make sound decisions today is dependent on how well they can predict the future. Getting the answers to these questions involves single-row calculations, time series analysis, and access to aggregated historical and current data. This requires OLAP -- online analytical processing.

Multidimensional technology is now available within the Oracle Database. Organizations no longer need to choose between a multidimensional OLAP database and a relational database. By integrating multidimensional tables and an analytic engine into the database, Oracle provides the power of multidimensional analysis along with the manageability, scalability, and reliability of the Oracle Database. The integration of multidimensional technology in a relational database is important because maintaining a standalone multidimensional database is costly. It requires additional hardware and DBAs who are skilled at using the specialized administrative tools of the multidimensional database. Moreover, standalone multidimensional databases require applications that use proprietary APIs. This severely limits the number of applications that can be run against them, not only because fewer applications are available in these APIs, but because all the data that they run on must be transferred from the relational database to the multidimensional database. These requirements often force enterprises into supporting two sets of query and reporting tools, one for the relational database and the other for the multidimensional database.

In contrast, the OLAP option is fully integrated into the Oracle Database. DBAs use the same tools to administer this option as they use to administer all other components of the database. The DBA can decide the best location for storing and calculating the data as part of optimizing the operations of the database. A single application can access both relational and multidimensional data.

SQL-based applications can now use pure SQL against information-rich relational views of multidimensional data provided by an OLAP-enabled Oracle Database. OLAP calculations can be queried using SQL, enabling application developers to leverage their investment in SQL while expanding the analytic sophistication of their software to include modeling, forecasting, and what-if analysis. Standard reporting applications can present the results of complex multidimensional calculations, while ad-hoc querying tools such as custom aggregate members and custom measures can expand the analyst's range of calculation functions.

See also OLTP, Data Warehouse

OLAP Analytic Engine

The OLAP analytic engine supports the selection and rapid calculation of multidimensional data. The status of an individual session persists to support a series of queries, which is typical of analytical applications; the output from one query is easily used as input to the next query. A comprehensive set of data manipulation tools supports modeling, aggregation, allocation, forecasting, and what-if analysis. The OLAP engine runs within the Oracle kernel.

OLAP API

The OLAP API is a Java-based programming interface for OLAP applications, and it supports Oracle Business Intelligence Beans, OLAP Spreadsheet Addin and Discoverer for OLAP. The purpose of the OLAP API is to facilitate the development of OLAP applications, which allow users to dynamically select, aggregate, calculate, and perform other analytical tasks on data through a graphical user interface. Typically, the user interface of an OLAP application displays data in multidimensional formats, such as graphs and crosstabs.

In general, OLAP applications are developed within the context of business intelligence and data warehousing systems, and the features of the OLAP API are optimized for this type of application. With the OLAP API, a Java application can access, manipulate, and display data in multidimensional terms. The OLAP API also makes it possible to define a query in a step-by-step process that allows for undoing individual query steps without reproducing the entire query. Such multistep queries are easy to modify and refine dynamically.

Using the OLAP API, an application can do the following:

• Establish a connection to a data store
• Explore the metadata to discover what data is available for viewing or analysis
• Create queries that specify and manipulate the data according to the needs of application users (for example, selecting, aggregating, and calculating data)
• Retrieve query results that are structured for display in multidimensional format.
• Modify existing queries, rather than totally redefine them, as application users refine their analyses.