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Indexed by:期刊论文

Date of Publication:2017-07-25

Journal:CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE

Included Journals:SCIE、EI、Scopus

Volume:29

Issue:14,SI

ISSN No.:1532-0626

Key Words:cloud storage; RDF; balanced clustering partition; multi-level label propagation

Abstract:The continuous growth of resource description framework (RDF) data poses an important challenge on RDF data partitioning that is a vital technique for effective cloud storage. Recently, many partitioning algorithms for large RDF data have been developed, and most of them are based on graph partitioning. However, existing graph partitioning methods could not partition asymmetric RDF data effectively, resulting in a lower performance for cloud storage. This paper proposes a balanced RDF graph partitioning algorithm for storing massive RDF data on cloud. We first devise a modularity-based multi-level label propagation algorithm (MMLP) to partition RDF graph roughly and then use a balanced K-mediods clustering algorithm for final k-way partitioning. Balanced RDF graph partitioning algorithm designs an effective label update rule and a balanced modification strategy to achieve a high quality coarsening result and make the partition as equilibrium as possible. Experiments are carried on two representative RDF benchmarks and one real RDF dataset by comparison with two representative graph partitioning methods, that is, METIS and MLP+METIS. Results demonstrate that our proposed scheme can produce a high-quality partition for massive RDF data storage on cloud. Copyright (C) 2016 John Wiley & Sons, Ltd.