document: Mind the data skew : distributed inferencing by speeddating in elastic regions Semantic Web data exhibits very skewed frequency distributions among terms . Efficient large-scale distributed reasoning methods should maintain load-balance in the face of such highly skewed distribution of input data . We show that term-based partitioning , used by most distributed reasoning approaches , has limited scalability due to load-balancing problems . We address this problem with a method for data distribution based on clustering in elastic regions . Instead of as - signing data to fixed peers , data flows semi-randomly in the network . Data items `` speed-date '' while being temporarily collocated in the same peer . We introduce a bias in the routing to allow semantically clustered neighborhoods to emerge . Our approach is self-organising , efficient and does not require any central coordination . We have implemented this method on the MaRVIN platform and have performed experiments on large real-world datasets , using a cluster of up to 64 nodes . We compute the RDFS closure over different datasets and show that our clustering algorithm drastically reduces computation time , calculating the RDFS closure of 200 million triples in 7.2 minutes .
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