A Transformational Framework for the Automatic Control of Derived Data

This paper investigates the specification, implementation and application of derived data in the context of a functional/binary association data model. A framework for the automatic maintenance of derived data is presented. This framework is based on the transformational techniques of finite differencing in which repeated costly computations are replaced by more efficient incremental counterparts. Applications of this approach to summary data, integrity control, and triggers are discussed.

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