Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database. 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. Amazon.com: Data Warehousing, Data Mining, and OLAP (Data Warehousing/Data Management) (9780070062726): Alex Berson, Stephen J. Smith: Books3.8/5(10).
Jan 25, 2018 · General OLAP operations involve Roll-up, Roll-down, Pivot, and Slice-and-Dice. Here we'd like to expand the list and look through all possible OLAP operations. OLAP and Data Mining. In large data warehouse environments, many different types of analysis can occur. In addition to SQL queries, you may also apply more advanced analytical operations to your data. Two major types of such analysis are OLAP (On-Line Analytic Processing) and data mining. Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database. 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. Amazon.com: Data Warehousing, Data Mining, and OLAP (Data Warehousing/Data Management) (9780070062726): Alex Berson, Stephen J. Smith: Books3.8/5(10).
OLAP and Data Mining
In large data olap in data mining environments, many different types of analysis can occur. In addition to SQL queries, you may also apply more advanced analytical operations to your data. Two major types of such analysis are OLAP (On-Line Analytic Olap in data mining 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 are options to the Oracle9i Database. This chapter provides a brief introduction to these technologies, and more detail can be found in these products' respective documentation.
The following topics provide an introduction to Oracle's OLAP and data mining capabilities:
Oracle9i OLAP adds the query performance and calculation capability previously found only in multidimensional databases to Oracle's relational platform. In addition, it provides a Java OLAP API that is appropriate for the development of internet-ready analytical applications. Unlike other combinations of OLAP and RDBMS technology, olap in data mining, Oracle9i OLAP is not a olap in data mining database using bridges to move data from the relational data store to a multidimensional data store, olap in data mining. Instead, it is truly an OLAP-enabled relational database. As a result, Oracle9i provides the benefits of a multidimensional database along with the scalability, accessibility, security, manageability, and high availability of the Oracle9i database. The Java OLAP API, which is specifically designed for internet-based analytical applications, olap in data mining, offers productive data access.
Benefits of OLAP and RDBMS Integration
Basing an OLAP system directly on the Oracle server offers the following benefits:
Oracle9i OLAP is highly scalable. In today's environment, there is tremendous growth along three dimensions of analytic applications: number of users, size of data, complexity of analyses. There are more users of analytical applications, and they need access to more data to perform more sophisticated analysis and target marketing. For example, a telephone company might want a olap in data mining dimension to include detail such as all telephone numbers as part of an application that is used to analyze customer turnover. This would require support for multi-million row dimension tables and very large volumes of fact data. Oracle9i can handle very large data sets using parallel execution and partitioning, as well as offering support for advanced hardware and clustering.
Oracle9i includes many features that support high availability. One of the most significant is partitioning, which allows management of precise subsets of tables and indexes, so that management operations affect only small pieces of these data structures. By partitioning tables and indexes, data management processing time is reduced, thus minimizing the time data is unavailable. Another feature supporting high availability is transportable tablespaces. With transportable tablespaces, large data sets, including tables and indexes, can be added with almost no processing to other databases. This enables extremely rapid data loading and updates.
Oracle enables you to precisely control resource utilization, olap in data mining. The Database Resource Manager, for example, provides a mechanism for allocating the resources of a data warehouse among different sets of end-users. Consider an environment where the marketing department and the sales department share an OLAP system. Using the Database Resource Manager, you could specify that the marketing department receive at least 60 percent of the CPU resources of the machines, while the sales department receive 40 percent of the CPU resources. You can also further specify limits on the total number of active sessions, and the degree of parallelism of individual queries for each department.
Another resource management facility is the progress monitor, which gives end users and administrators the status of long-running olap in data mining. Oracle9i maintains statistics describing the percent-complete of these operations. Oracle Enterprise Manager enables you to view a bar-graph display of these operations showing what percent complete they are. Moreover, any other tool or any database administrator can also retrieve progress information directly from the Oracle data server, using system views.
Backup and Recovery
Oracle provides a server-managed infrastructure for backup, restore, and recovery tasks that enables simpler, olap in data mining, safer operations at terabyte scale. Some of the highlights are:
- Details related to backup, restore, olap in data mining, and recovery operations are maintained by the server in a recovery catalog and automatically used as part of these operations. This reduces administrative burden and minimizes the possibility of human errors.
- Backup and recovery operations are fully integrated with partitioning. Individual partitions, when placed in olap in data mining own tablespaces, can be backed up and restored independently of the other partitions of a table.
- Oracle includes support for incremental backup and recovery using Recovery Manager, monero core mining operations to be completed efficiently within times proportional to the amount of changes, rather than the overall size of the database.
- The backup and recovery technology is highly scalable, and provides tight interfaces to industry-leading media management subsystems, olap in data mining. This provides for efficient operations that can scale up to handle very large volumes of data. Open Platforms for more hardware options & enterprise-level platforms.
Just as the demands of real-world transaction processing required Oracle to develop robust features for scalability, manageability and backup and recovery, they lead Oracle olap in data mining worker 1 mining industry-leading security features, olap in data mining. The security features in Oracle have reached the highest levels of U.S. government certification for database trustworthiness. Oracle's fine grained access control feature, enables cell-level security for OLAP users. Fine grained access control works with minimal burden on query processing, and it enables efficient centralized security management.
Oracle enables data mining inside the database for performance and scalability. Some of the capabilities are:
- An API that provides programmatic control and application integration
- Analytical capabilities with OLAP and statistical functions in the database
- Multiple algorithms: Naïve Bayes, decision trees, clustering, and association rules
- Real-time and batch scoring modes
- Multiple prediction types
- Association insights
Oracle Data Mining documentation for more information
Enabling Data Mining Applications
Oracle9i Data Mining provides a Java API to exploit the data mining functionality that is embedded within the Oracle9i database.
By delivering complete programmatic control of the database in data mining, Oracle Data Mining (ODM) delivers powerful, scalable modeling and real-time scoring. This enables e-businesses to incorporate predictions and classifications in all processes and decision points throughout the business cycle.
ODM is designed to meet the challenges of vast amounts of data, olap in data mining, delivering accurate insights completely integrated into e-business applications. This integrated intelligence enables the automation and decision speed biggest mining excavator e-businesses require in order to compete today.
Predictions and Insights
Oracle Data Mining uses data mining algorithms to sift through the large volumes of data generated by e-businesses to produce, evaluate, and deploy predictive models. It also enriches mission critical applications in CRM, manufacturing control, inventory management, olap in data mining service and support, Web portals, wireless devices and other fields with context-specific recommendations and predictive monitoring of critical processes. ODM delivers real-time answers to questions such as:
- Which N items is person A most likely to buy or like?
- What is the likelihood that this product will be returned for repair?
Mining Within the Database Architecture
Oracle Data Mining performs all blockchain mining process phases of data mining within the database. In each data mining phase, this architecture results in significant improvements including performance, automation, and integration.
Data preparation can create new tables or views of existing data. Both options perform faster than moving data to an external data mining utility and offer the programmer the option of snap-shots or real-time updates.
Oracle Data Mining provides utilities for complex, data mining-specific tasks. Binning improves model build time and model performance, so ODM provides a utility for user-defined binning. ODM accepts data in either single record format or in transactional format and performs mining on transactional formats. Single record format is most common in applications, so ODM provides a utility for transforming single record format.
Associated analysis for preparatory data exploration and model evaluation is extended by Oracle's statistical functions and OLAP capabilities. Because these also operate within the database, they can all be incorporated into a seamless application that shares database objects. This allows for more functional and faster applications.
Oracle Data Mining provides four algorithms: Naïve Bayes, Decision Tree, Clustering, and Association Rules. These algorithms address a broad spectrum of business problems, ranging from predicting the future likelihood of a customer purchasing a given product, to understand which products are likely be purchased together in a single trip to the grocery store. Olap in data mining model building takes place inside the database. Once again, the data does not need to move outside the database in order to build the model, and therefore the entire data-mining process is accelerated.
Models are stored in the database and directly accessible for evaluation, reporting, and olap in data mining analysis by a wide variety of tools and application functions. ODM provides APIs for calculating traditional confusion matrixes and lift charts. It stores the models, the underlying data, and these analysis results together in the database to allow further analysis, reporting and application specific model management.
Oracle Data Mining provides both batch and real-time scoring. In batch mode, ODM takes a table as input. It scores every record, and returns a scored table as a result. In real-time mode, parameters for a single record are passed in and the scores are returned in a Java object.
In both modes, ODM can deliver a variety of scores. It can return a rating or probability of a specific outcome. Alternatively it can return a predicted outcome and the probability of that outcome occurring. Some examples follow.
- How likely is this event to end in outcome A?
- Which outcome is most likely to result from this event?
- What is the probability of each possible outcome for this event?
The Oracle Data Mining API lets you build analytical models and deliver real-time predictions in any application that supports Java. The API is based on the emerging JSR-073 standard.
Data mining - WikipediaAmazon.com: Data Warehousing, Data Mining, and OLAP (Data Warehousing/Data Management) (9780070062726): Alex Berson, Stephen J. Smith: Books3.8/5(10).
OLAP and Data Mining. In large data warehouse environments, many different types of analysis can occur. In addition to SQL queries, you may also apply more advanced analytical operations to your data. Two major types of such analysis are OLAP (On-Line Analytic Processing) and data mining. Amazon.com: Data Warehousing, Data Mining, and OLAP (Data Warehousing/Data Management) (9780070062726): Alex Berson, Stephen J. Smith: Books3.8/5(10). Jan 25, 2018 · General OLAP operations involve Roll-up, Roll-down, Pivot, and Slice-and-Dice. Here we'd like to expand the list and look through all possible OLAP operations.
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.
OLAP Technology in the Oracle Database
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.
Full Integration of Multidimensional Technology
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.
Ease of Application Development
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.
Ease of Administration
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.
Unmatched Performance and Scalability
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.
Querying Dimensional Objects
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.
Tools for Creating and Managing Dimensional Objects
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.
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