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We introduce a framework that supports archivists in planning and running migrations. The central idea is that -once relevant information pieces of digital documents are modeled -desired migration results can be specified by means of preservation constraints. From these constraint specifications we are able to derive migration algorithms that provably respect a set of document properties before (pre-conditions) and after migration (post-conditions). Underlying is the concept of Abstract State Machines (ASM) modeling archival states. Migrations are modeled as sequences of basic operations that change the archive's state while respecting userdefined constraints. Among others, our target scenarios comprise legal and medical documents where considerable property changes cannot be tolerated and where constraint preservation must hold over a long period of time.

We present and evaluate a novel constraint based model transformation approach that implements a preservation-centric view. The proposed framework comprises formal preservation constraints that can be used to specify the preservation of invariants that are possibly implemented differently in the source and target model. These invariants are enclosed in concepts, which at the same time serve as grouping mechanism for their different implementations. In that, our framework abstracts from the concrete implementation languages by pre-supposing only a basic set of modeling constructs. To this end, we present two case studies where we apply our approach for the preservation of non-trivial properties and provide some performance analysis where we show that tracking the preservation of a relevant class of complex properties can be done in linear time.

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