A map-reduce framework for change propagation in geographic databases

Updating a schema is a very important activity which occurs naturally during the life cycle of database systems, due to different causes. A challenging problem arising when a schema evolves is the change propagation problem, i.e. the updating of the database ground instances to make them consistent with the evolved schema. Spatial datasets, a stored representation of geographical areas, are VLDBs and so the change propagation process, involving an enormous mass of data among geographical distributed nodes, is very expensive and call for efficient processing. Moreover, the problem of designing languages and tools for spatial data sets change propagation is relevant, for the shortage of tools for schema evolution, and, in particular, for the limitations of those for spatial data sets. In this paper, we take in account both efficiency and limitations and we propose an instance update language, based on the efficient and popular Map-Reduce Google programming paradigm, which allows to perform in a parallel way a wide category of schema changes. A system embodying the language has been implementing