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A conformed dimension is a set of data attributes that have been physically referenced in multiple database tables using the same key value to refer to the same structure, attributes, domain values, definitions and concepts. A conformed dimension cuts across many facts.

Dimensions are often recycled for multiple applications within the same database. For instance, a "Date" dimension can be used for "Date of Sale", as well as "Date of Delivery", or "Date of Hire". This is often referred to as a "role-playing dimension".

racle SQL Developer Data Modeler provides a model driven approach for database design and generation, implemented by integrated set of models – Logical, Data types, Dimensional, Relational, Data Flow diagrams and Physical models for supported Oracle Databases, Microsoft SQL Server 2000 and 2005

Cube Views metadata

Dimensional Models   Dedicated full featured Dimensional model – star and snowflake schema easily can be built and expressed on detailed and compact diagrams   Dimensions – support for merging (level can belongs to more than one dimension), shared, fact and role playing dimensions   Hierarchies – value based hierarchies (parent-child), and regular and ragged level based hierarchies   Measures – fully, semi and none additive; different aggregation functions on different dimensions ; fact dimension; calculated measures   Query wizard allows Select statements to be generated from the dimensional model   Support for Oracle OLAP. This includes specifics like cube partitioning, sparse dimensions, and compressed measures   Built-in wizards help to define all required object types and view definitions that enable SQL access to dimensional data in Oracle AW (using the OLAP_TABLE interface)   Bidirectional integration with Oracle physical model - Dimensional model can be created using SQL dimension definitions in physical model or the definitions can be created from the dimensional model

A cube can be considered a generalization of a three-dimensional spreadsheet.

Each cell of the cube holds a number that represents some measure of the business

OLAP data is typically stored in a star schema or snowflake schema in a relational data warehouse or in a special-purpose data management system.

The usual interface to manipulate an OLAP cube is a matrix interface like Pivot tables

MOLAP stores this data in an optimized multi-dimensional array storage, rather than in a relational database

The problem of deciding which aggregations (views) to calculate is known as the view selection problem. View selection can be constrained by the total size of the selected set of aggregations, the time to update them from changes in the base data, or both. The objective of view selection is typically to minimize the average time to answer OLAP queries, although some studies also minimize the update time

Dr. Kimball explains how Hadoop can be both: A destination data warehouse, and also An efficient staging and ETL source for an existing data warehouse

Building a Hadoop Data Warehouse: Hadoop 101 for EDW Professionals Dr. Ralph Kimball explains how Hadoop can be both a destination data warehouse, and also an efficient staging and ETL source for an existing data warehouse. Learn how enterprise conformed dimensions can be used as the basis for integrating Hadoop and conventional data warehouses.

Unleash Targeted Insights through Advanced Analytics As hospitals and health systems continue to improve the patient experience, it can be more challenging to identify specific initiatives to drive improvement. Press Ganey’s advanced analytics help health care leaders spark new initiatives by viewing opportunities through a new lens. Sophisticated techniques, such as segmentation, text analysis, multi-dimensional modeling and cluster analysis, evaluate multiple dimensions to uncover relationships, patterns and new insights to reveal targeted opportunities to improve