论文标题
wawpart：知识图的工作负载分区
WawPart: Workload-Aware Partitioning of Knowledge Graphs
论文作者
论文摘要
如今，以知识图的形式以知识图的形式使用的大规模数据集使用。知识图通常超过单个计算机系统的容量，尤其是如果必须将图存储在主内存中。为了克服这一点，知识图可以分为多个子图，并分布在许多计算节点中。但是，因此，在图形上执行的许多常见任务的执行，例如查询，因此受到了苦难。这是由于图形边缘交叉（切割）划分的分布式连接。在本文中，我们提出了一种知识图分区的方法，该方法考虑了一组查询（工作负载）。最终的分区旨在减少分布式连接的数量并提高工作量性能。查询工作负载和知识图中确定的关键特征用于聚集查询，然后分区图。重写查询以解释图形分区。我们的评估结果证明了工作负载处理时间的绩效提高。
Large-scale datasets in the form of knowledge graphs are often used in numerous domains, today. A knowledge graphs size often exceeds the capacity of a single computer system, especially if the graph must be stored in main memory. To overcome this, knowledge graphs can be partitioned into multiple sub-graphs and distributed as shards among many computing nodes. However, performance of many common tasks performed on graphs, such as querying, suffers, as a result. This is due to distributed joins mandated by graph edges crossing (cutting) the partitions. In this paper, we propose a method of knowledge graph partitioning that takes into account a set of queries (workload). The resulting partitioning aims to reduces the number of distributed joins and improve the workload performance. Critical features identified in the query workload and the knowledge graph are used to cluster the queries and then partition the graph. Queries are rewritten to account for the graph partitioning. Our evaluation results demonstrate the performance improvement in workload processing time.
