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Browse Prior Art Database

Data Driven Data Warehouse Metadata Model Engine

IP.com Disclosure Number: IPCOM000240917D
Publication Date: 2015-Mar-12
Document File: 3 page(s) / 41K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is a method that enables a meta-data engine to automatically create a semantic meta-data model based upon intentional instructions or tags in the structure and design of the relational database.

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Data Driven Data Warehouse Metadata Model Engine

Business intelligence systems that are based upon relational database systems often utilize dimensional structures in the designated business metadata layer . These types of systems are often vulnerable to underlying changes in the relational database structure. When the underlying relational database undergoes structural changes , the resulting business metadata layer becomes outdated or even broken . The traditional solution requires a trained metadata model designer to manually redesign the metadata model to conform to the new relational model .

In this article, dimensions and measures are the results of data aggregation in business intelligence systems. A measure is a data point that is aggregated. Dimensions are the context that help the user of measures understand the meaning of those measures . A tag is a label attached to someone or something to provide identification or other information.

Typically, an individual who is trained in meta-data modeling manually maps dimensional structures to a relational database model .

The novel contribution is a method that enables a meta -data engine to automatically create a semantic meta-data model based upon intentional instructions or tags in the structure and design of the relational database . The tags appear in a relational database structure as standardized labels in names , descriptions, or note properties of relational tables and field structures.

The tags instruct the meta-data model engine to identify a field or table as :

• a Measure • a Dimension • the dimensional hierarchy to which the dimension belongs

• the level in the dimensional hierarchy • the sort order of a dimensional level • the primary identifying key for a dimensional level • the label or caption for a member of a dimensional level • the data formatting for a measure
• the type of default aggregation to apply to a measure (e.g., sum, average, standard deviation, etc.)

The meta-data engine reads these tags in the fields and tables of the relational model and creates an automatic business semantic data wareho...