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Summary 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 © 2016 John Wiley & Sons, Ltd.

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The quick and intelligent requests and answers in artificial intelligence (AI) are inseparable from intelligent data. Knowledge graph makes data more intelligent by establishing association among data, which provides convenience for intelligent search, reasoning and analysis of data. Resource Description Framework (RDF) is an effective data representation model of knowledge graph. This paper takes RDF as the research object and proposes an incremental partition method of intelligent data (IPID) to realize the distributed storage of large-scale AI data. First, IPID gives a mixed object function integrating edge cut and load balancing. Second, IPID devises the initial and incremental partitioning algorithms of RDF. The initial partition divides the original RDF graph into kernel vertices, boundary vertices and free vertices. The boundary and freedom nodes select the kernel vertex with the maximum gain of object function to form a sub-partition. And the incremental partition is in charge of the selection of sub-partition of new and deleted data by the object function. Meanwhile, the incremental partition algorithm would also execute a dynamic adjustment strategy at a certain time interval according to the balance and tightness of sub-partition to satisfy the partitioning object. Finally, IPID is tested on the knowledge graph datasets. The experimental results show that the object function guarantees the quality of knowledge graph partition in edge cut and load balancing, and effectively realizes the incremental partition.

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