Every relational schema can be mapped to an equivalent associative schema

Sentences provides this capability in the form of a a sophisticated wizard which automatically extracts SQL schemas, detects subtype and supertype relationships amongst business objects, and also captures the users' knowledge about their database.

Column and table names are cleansed of hyphens, underscores etc and turned into 'proper' language.

This capability lets you see your relational data as you've never seen it before.


The illustration below shows how data from the SQL Server AdventureWorks sample database appears in Sentences, less than five minutes after beginning the assimilation process.

The process has detected that the Employee table has four subsets - Department History, Pay History, Sales Person and Address, that Sales Person itself has two subsets: Quota History and Territory History.

The metacode dataform shown for Employee 3 is automatically inferred from the schema, It shows a tab for each subset and is fully input capable.



This capability can be used to integrate many relational databases simultaneously.

Seen through the medium of the associative model, similar types in different databases can be flagged as equivalent, which will aggregate their instances.


When two types are equivalent, instances that represent the same real-world things can also be flagged as equivalent, so "IBM" in one database can be equated with "International Business Machines" in anther.

Their attributes are then aggregated for data quality and cleansing purposes.

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