Table of Contents


  1. Preface
  2. Using the Designer
  3. Working with Sources
  4. Working with Flat Files
  5. Working with Targets
  6. Mappings
  7. Mapplets
  8. Mapping Parameters and Variables
  9. Working with User-Defined Functions
  10. Using the Debugger
  11. Viewing Data Lineage
  12. Comparing Objects
  13. Managing Business Components
  14. Creating Cubes and Dimensions
  15. Using the Mapping Wizards
  17. Datatype Reference
  18. Configure the Web Browser

Key Elements of Multi-Dimensional Metadata

Key Elements of Multi-Dimensional Metadata

The following table describes key elements of multi-dimensional metadata:
Pre-stored summary of data or grouping of detailed data which satisfies a specific business rule. Example rules: sum, min, count, or combinations of them.
A specific property of a dimension. Examples: size, type, and color.
A set of related factual measures, aggregates, and dimensions for a specific dimensional analysis problem. Example: regional product sales.
A set of level properties that describe a specific aspect of a business, used for analyzing the factual measures of one or more cubes which use that dimension. Examples: geography, time, customer, and product.
Drilling is the term used for navigating through a cube. This navigation is usually performed to access a summary level of information or to provide more detailed properties of a dimension in a hierarchy.
A fact is a time variant measurement of quantitative data in a cube; for example, units sold, sales dollars, or total profit.
Hierarchy concept refers to the level of granularity represented by the data in a particular dimension of a cube. For example, state, county, district, and city represent different granularity in the hierarchy of the geography dimension.
Means for representing quantitative data in facts or aggregates. Example measures are total sales or units sold per year.
A process used for reducing redundancies and removing anomalies in related dimension tables in various hierarchies.
Term used for referring to duplication of data among related tables for the sake of improving the speed of query processing.
Star Schema
A normalized multi-dimensional model in which each disjoint dimension is represented by a single table.
Snow Flake Schema
A normalized multi-dimensional model in which at least one dimension is represented by two or more hierarchically related tables.

Updated June 25, 2018