Manual Data Warehousing and Data Mining for Telecommunications

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It provides corporate wide data integration. The implementation cycle of data mart is generally measured in weeks rather than months or year. For efficient query processing, only some of the possible summary views may be materialized.

What is Oracle Communications Data Model?

The foundation of the data warehousing system is the data model. A good data model will allow the data warehousing system to grow easily, as well as allowing for good performance.

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In data warehousing project, the logical data model is built based on user requirements, and then it is translated into the physical data model:. In data warehousing , it is common for the conceptual data model and the logical data model to be combined into a single step deliverable. Data warehouse contains integrated and processed data to perform data analysis at the time of decision making and planning. It is a very important tool for business executives.

It supports associations, constructing analytical models, performing classification and predication, and presenting the mining results using crosstabs, graphs, and other visualization tools. An information extraction activity whose goal is to discover hidden facts contained in databases is termed as data mining.

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Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis. Data mining does not replace skilled business analysts or managers, but rather gives them powerful new tools to improve the job they are doing.

It is a something out from traditional tracks of decision making and business planning. It offers great promises in helping organizations to uncover patterns hidden in their data that can be used to predict the behavior of customers, products and processes.

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The pre-built, pretuned data model with intelligent insight into detailed communications and market data, allows you to quickly gain value from your data warehousing effort, supports diverse analytical requirements, and assists in building future analytical applications. Fast, easy and predictable implementation reduces risks and enables you to achieve strategic value more rapidly by eliminating deployment delays and expenses associated with built-from-scratch or proprietary data warehouse solutions. Oracle Communications Data Model includes the following components:.

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  • Data Warehousing and Data Mining for Telecommunications : Robert M. Mattison : ;

Chapter 2, "Logical Data Model Foundation" describes the logical data model. Chapter 3, "Logical Data Model Dimensions" describes the dimensions. Several Oracle technologies are involved in building the infrastructure for telecommunications business intelligence.

Data Mining - Applications & Trends

Oracle Communications Data Model utilizes a complete Oracle technical stack. It leverages the following data warehousing features of the Oracle database: SQL model, compression, partitioning, advanced statistical functions, materialized views, data mining, and online analytical processing OLAP. Use the Oracle tools shown in Table to customize the predefined logical and physical models provided with Oracle Communications Data Model, or to populate the target relational tables, materialized views, or OLAP cubes.

Oracle Business Intelligence Suite Enterprise Edition is a comprehensive suite of enterprise Business Intelligence products that delivers a full range of analysis and reporting capabilities. Skip Headers. Oracle Communications Data Model provides the following features: Query and Reporting for information: provides extraction of detailed and summary data. OLAP for data analysis: provides summaries, trends, and forecasts. Book List.

Introducing Oracle Communications Data Model

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