| Oracle® Database Data Warehousing Guide 11g Release 1 (11.1) Part Number B28313-01 |
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View PDF |
| Oracle® Database Data Warehousing Guide 11g Release 1 (11.1) Part Number B28313-01 |
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|
View PDF |
In large data warehouse environments, many different types of analysis can occur. You can enrich your data warehouse with advance analytics using OLAP (On-Line Analytic Processing) and data mining. Rather than having a separate OLAP or data mining engine, Oracle has integrated OLAP and data mining capabilities directly into the database server. Oracle OLAP and Oracle Data Mining (ODM) are options to the Oracle Database. This chapter provides a brief introduction to these technologies, and more detail can be found in their respective documentation.
The following topics provide an introduction to Oracle's OLAP and data mining capabilities:
OLAP and data mining are used to solve different kinds of analytic problems:
OLAP provides summary data and generates rich calculations. For example, OLAP answers questions like "How do sales of mutual funds in North America for this quarter compare with sales a year ago? What can we predict for sales next quarter? What is the trend as measured by percent change?"
Data mining discovers hidden patterns in data. Data mining operates at a detail level instead of a summary level. Data mining answers questions like "Who is likely to buy a mutual fund in the next six months, and what are the characteristics of these likely buyers?"
OLAP and data mining can complement each other. For example, OLAP might pinpoint problems with sales of mutual funds in a certain region. Data mining could then be used to gain insight about the behavior of individual customers in the region. Finally, after data mining predicts something like a 5% increase in sales, OLAP can be used to track the net income. Or, Data Mining might be used to identify the most important attributes concerning sales of mutual funds, and those attributes could be used to design the data model in OLAP.
See Also:
"Data Mining" for an introduction to data mining
Oracle Data Mining Concepts for further information
Oracle OLAP User's Guide for further information
Oracle OLAP uses a multidimensional data model to perform complex statistical, mathematical, and financial analysis of historical data in real time. Oracle OLAP is fully integrated in the database, so that you can use standard SQL administrative, querying, and reporting tools.
For more information regarding OLAP, see Oracle OLAP User's Guide.
Oracle Database offers the industry's first and only embedded OLAP server. Oracle OLAP provides native multidimensional storage and speed-of-thought response times when analyzing data across multiple dimensions. The database provides rich support for analytics such as time series calculations, forecasting, advanced aggregation with additive and non additive operators, and allocation operators. These capabilities make the Oracle database a complete analytical platform, capable of supporting the entire spectrum of business intelligence and advanced analytical applications.
By integrating multidimensional objects and analytics into the database, Oracle provides the best of both worlds: the power of multidimensional analysis along with the reliability, availability, security, and scalability of the Oracle database.
Oracle OLAP is fully integrated into Oracle Database. At a technical level, this means:
The OLAP engine runs within the kernel of Oracle Database.
Dimensional objects are stored in Oracle Database in their native multidimensional format.
Cubes and other dimensional objects are first class data objects represented in the Oracle data dictionary.
Data security is administered in the standard way, by granting and revoking privileges to Oracle Database users and roles.
Applications can query dimensional objects using SQL.
The benefits to your organization are significant. Oracle OLAP offers the power of simplicity. One database, standard administration and security, standard interfaces and development tools.
Oracle OLAP makes it easy to enrich your database and your applications with interesting analytic content. Native SQL access to Oracle multidimensional objects and calculations greatly eases the task of developing dashboards, reports, business intelligence (BI) and analytical applications of any type compared to systems that offer proprietary interfaces. Moreover, SQL access means that the power of Oracle OLAP analytics can be used by any database application, not just by the traditional limited collection of OLAP applications.
Because Oracle OLAP is completely embedded in the Oracle database, there is no administration learning curve as is typically associated with standalone OLAP servers. You can leverage your existing DBA staff, rather than invest in specialized administration skills.
One major administrative advantage of Oracle's embedded OLAP technology is automated cube maintenance. With standalone OLAP servers, the burden of refreshing the cube is left entirely to the administrator. This can be a complex and potentially error-prone job. The administrator must create procedures to extract the changed data from the relational source, move the data from the source system to the system running the standalone OLAP server, load and rebuild the cube. The DBA must take responsibility for the security of the deltas (changed values) during this process as well.
With Oracle OLAP, in contrast, cube refresh is handled entirely by the Oracle database. The database tracks the staleness of the dimensional objects, automatically keeps track of the deltas in the source tables, and automatically applies only the changed values during the refresh process. The DBA simply schedules the refresh at appropriate intervals, and Oracle Database takes care of everything else.
With Oracle OLAP, standard Oracle Database security features are used to secure your multidimensional data.
In contrast, with a standalone OLAP server, administrators must manage security twice: once on the relational source system and again on the OLAP server system. Additionally, they must manage the security of data in transit from the relational system to the standalone OLAP system.
Business intelligence and analytical applications are dominated by actions such as drilling up and down hierarchies and comparing aggregate values such as period-over-period, share of parent, projections onto future time periods, and a myriad of similar calculations. Often these actions are essentially random across the entire space of potential hierarchical aggregations. Because Oracle OLAP pre-computes or efficiently computes on the fly all aggregates in the defined multidimensional space, it delivers unmatched performance for typical business intelligence applications.
Oracle OLAP queries take advantage of Oracle shared cursors, dramatically reducing memory requirements and increasing performance.
When Oracle Database is installed with Real Application Clusters (RAC), OLAP applications receive the same benefits in performance, scalability, fail over, and load balancing as any other application.
All these features add up to reduced costs. Administrative costs are reduced because existing personnel skills can be leveraged. Moreover, the Oracle database can manage the refresh of dimensional objects, a complex task left to administrators in other systems. Standard security reduces administration costs as well. Application development costs are reduced because the availability of a large pool of application developers who are SQL knowledgeable, and a large collection of SQL-based development tools means applications can be developed and deployed more quickly. Any SQL-based development tool can take advantage of Oracle OLAP. Hardware costs are reduced by Oracle OLAP's efficient management of aggregations, use of shared cursors, and Oracle RAC, which enables highly scalable systems to be built from low-cost commodity components.
Oracle OLAP adds power to your SQL applications by providing extensive analytic content and fast query response times. A SQL query interface enables any application to query cubes and dimensions without any knowledge of OLAP.
The OLAP option automatically generates a set of relational views on cubes, dimensions, and hierarchies. SQL applications query these views to display the information-rich contents of these objects to analysts and decision makers. You can also create custom views that comply with the structure expected by your applications, using the system-generated views like base tables.
Analysts can choose any SQL query and analysis tool for selecting, viewing, and analyzing the data You can use your favorite tool or application, or use one of the tools supplied with Oracle Database, such as Oracle Application Express and Business Intelligence Publisher.
Analytic Workspace Manager is the primary tool for creating, developing, and managing dimensional objects in Oracle Database.
Oracle OLAP is contained in the database and its resources are managed using the same tools, such as Oracle Enterprise Manager Database Control, Automatic Workload Repository, and Automatic Database Diagnostic Monitor.
