Distributed Knowledge Graph Query Acceleration Algorithm

As the era of big data continues to evolve, the scale of knowledge data that needs to be processed in reality is enormous, and the single-machine model is incapable of handling queries on large-scale knowledge graph data. Therefore, distributed clusters are necessary to improve processing capability. The core of the existing approaches is all by splitting the large-scale graph data into multiple copies, distributing each copy to different machines for processing, and finally merging the results. However, these approaches suffer from two problems: (i) the result of knowledge graph merging is huge, far exceeding the final result itself, resulting in a lot of data transfer overhead during the distributed merging phase; (ii) the parallelism of algorithms is limited to the physical level of machine parallelism in task partitioning and lacks computational logic parallelism, such as the merging phase, which does not achieve good parallelism. To address these issues, we propose a distributed framework for offline index construction and online SPARQL query processing framework to achieve parallel accelerated processing. Our approach can more efficiently filter candidate solutions that do not match the result, reducing the size of the results to be merged and leading to a reduction in computational and communication costs. Additionally, we also introduce additional parallelism in the mutual merging phase to improve computational efficiency and system throughput.