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By using semantic modeling it is possible to represent organization
structures and present the information very fast and accurate.
In addition, the semantic model is flexible enough to allow
immediate implementation of changes. Techniques, like entity/relationship
modeling, are database modeling techniques opposed to semantic
modeling, being a data modeling technique.
Basically, a semantic
model consists of dimensions and variables. Dimensions are
notions like departments, products, brands, markets, countries
and time-periods. Variables consist of key business information
like purchase price, cost, items sold and market shares. The
semantic model also describes the hierarchy of the dimensions
and the relationships.
Advantages of Semansys'
semantic modeling are:
- Clear distinction
between business context and information technology
- One-to-one mapping
between business context and model
- No technical
jargon or specialist knowledge required
- No limitations
in the number of dimensions (no cube)
- Easy to setup
and update
- No programming
required
- Makes 'cubes'
redundant
- Provides direct
access to data sources
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