  Many LOD datasets, such as DBpedia and LinkedGeoData, are voluminous and
process large amounts of requests from diverse applications. Many data products
and services rely on full or partial local LOD replications to ensure faster
querying and processing. While such replicas enhance the flexibility of
information sharing and integration infrastructures, they also introduce data
duplication with all the associated undesirable consequences. Given the
evolving nature of the original and authoritative datasets, to ensure
consistent and up-to-date replicas frequent replacements are required at a
great cost. In this paper, we introduce an approach for interest-based RDF
update propagation, which propagates only interesting parts of updates from the
source to the target dataset. Effectively, this enables remote applications to
`subscribe' to relevant datasets and consistently reflect the necessary changes
locally without the need to frequently replace the entire dataset (or a
relevant subset). Our approach is based on a formal definition for
graph-pattern-based interest expressions that is used to filter interesting
parts of updates from the source. We implement the approach in the iRap
framework and perform a comprehensive evaluation based on DBpedia Live updates,
to confirm the validity and value of our approach.
