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Change data capture of large-scale RDF data

We designed and implemented a method for change data capture tracking large-scale RDF datasets that change in time. The method allows efficient offline reconstruction of a view of data valid at a given time from a backlog of explicit change events. It operates on a temporal data model based on RDF and named graphs. It is built with semantic web standards and implemented via SPARQL 1.1 Update. We provide an efficient open-source implementation of the method in Halyard Bulk Update, a MapReduce application for the Halyard RDF store. This implementation allows to synchronize data via change data capture in a horizontally-scalable cluster on commodity hardware. We demonstrate the method on PUBMED, a public dataset aggregating rich metadata on biomedical literature.