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COLD. Revisiting Hub Labels on the Database for Large-Scale Graphs

机译:冷。重访数据库中用于大型图的中心标签

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Shortest-path computation is a well-studied problem in algorithmic theory. An aspect that has only recently attracted attention is the use of databases in combination with graph algorithms to compute distance queries on large graphs. To this end, we propose a novel, efficient, pure-SQL framework for answering exact distance queries on large-scale graphs, implemented entirely on an open-source database system. Our COLD framework (COmpressed Labels on the Database) may answer multiple distance queries (vertex-to-vertex, one-to-many, kNN, RkNN) not handled by previous methods, rendering it a complete solution for a variety of practical applications in large-scale graphs. Experimental results will show that COLD outperforms previous approaches (including popular graph databases) in terms of query time and efficiency, while requiring significantly less storage space than previous methods.
机译:最短路径计算是算法理论中经过充分研究的问题。直到最近才引起人们注意的一个方面是将数据库与图算法结合使用来计算大型图上的距离查询。为此,我们提出了一种新颖,高效,纯SQL的框架,用于回答大规模图形上的精确距离查询,该框架完全在开源数据库系统上实现。我们的COLD框架(数据库上的COmpressed标签)可以回答以前的方法无法处理的多个距离查询(顶点到顶点,一对多,kNN,RkNN),从而使其成为针对各种实际应用的完整解决方案。大型图。实验结果表明,COLD在查询时间和效率方面都优于以前的方法(包括流行的图形数据库),同时所需的存储空间比以前的方法要少得多。

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