Abstract
In this era of big data, data are diversified, strongly connected, fragmented, dynamic, and combined with dynamic knowledge fragments to optimize the distributed storage of graphs and enable fast and efficient knowledge graph query problems. Presently, the distributed storage scheme of graph data has a large number of hop accesses between partitions, which leads to a long retrieval response time and is not conducive to fragment knowledge expansion. According to the characteristics of real-time inflow knowledge fragments and the storage structure and principles of graph databases, the Metis+ algorithm is proposed. The label graph is used as the initial initialization segmentation graph, and it is roughened to reduce the cutting of the large-weight edge. The weighted LND algorithm is proposed to run the balancing strategy for storage and assign the similar nodes and closely related nodes to the same partition to the greatest extent, which minimizes jump accesses between the partitions during retrieval.
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Jingbin, W., Jing, L. (2019). Graph Data Retrieval Algorithm for Knowledge Fragmentation. In: Ni, W., Wang, X., Song, W., Li, Y. (eds) Web Information Systems and Applications. WISA 2019. Lecture Notes in Computer Science(), vol 11817. Springer, Cham. https://doi.org/10.1007/978-3-030-30952-7_43
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DOI: https://doi.org/10.1007/978-3-030-30952-7_43
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