Schema Evolution publication categorizer

More than ever before schema transformation is a prevalent problem that needs to be addressed to accomplish for example the migration of legacy systems to the newer OODB systems, the generation of structured web pages from data in database systems, or the integration of systems with different native data models. Such schema transformations are typically composed of a sequence of schema evolution operations. The execution of such sequences can be very time-intensive, possibly requiring many hours or even days and thus effectively making the database unavailable for unacceptable time spans. While researchers have looked at the deferred execution approach for schema evolution in an effort to improve availability of the system, to the best of our knowledge ours is the first effort to provide a direct optimization strategy for a sequence of changes. In this paper, we propose heuristics for the iterative elimination and cancellation of schema evolution primitives as well as for the merging of database modifications of primitives such that they can be performed in one efficient transformation pass over the database. In addition we show the correctness of our optimization approach, thus guaranteeing that the initial input and the optimized output schema evolution sequence produce the same final schema and data state. We provide proof of the algorithm's optimality by establishing the confluence property of our problem search space, i.e., we show that the iterative application of our heuristics always terminates and converges to a unique minimal sequence. Moreover, we have conducted experimental studies that demonstrate the performance gains achieved by our proposed optimization technique over previous solutions.