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The authors consider the setting of a Semantic Web database, containing both explicit data encoded in RDF triples, and implicit data, implied by the RDF semantics. Based on a query workload, they address the problem of selecting a set of views to be materialized in the database, minimizing a combination of query processing, view storage, and view maintenance costs. Starting from an existing relational view selection method, they devise new algorithms for recommending view sets, and show that they scale significantly beyond the existing relational ones when adapted to the RDF context.
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