论文标题
awapart：自适应工作负载感知知识图的分区
AWAPart: Adaptive Workload-Aware Partitioning of Knowledge Graphs
论文作者
论文摘要
在许多领域，大规模知识图越来越普遍。它们的大尺寸通常超过将图形存储在集中式数据存储中的系统限制，尤其是放置在主内存中时。为了克服这一点，大型知识图需要分为多个子图，并将其放置在分布式系统中的节点中。但是，由于涉及切割边缘的分布连接而引起的这些零散的子图纸提出了新的挑战，例如增加的沟通成本。为了解决这些问题，良好的分区应在考虑给定的查询工作量时减少边缘削减。但是，需要不断重新分配分区图，以适应查询工作量的更改并保持良好的平均处理时间。在本文中，引入了用于大规模知识图的自适应分区方法，该方法适应了分区，以应对查询工作负载的变化。我们的评估表明，在动态调整知识图三元组的分配后，查询处理时间的性能得到改善。
Large-scale knowledge graphs are increasingly common in many domains. Their large sizes often exceed the limits of systems storing the graphs in a centralized data store, especially if placed in main memory. To overcome this, large knowledge graphs need to be partitioned into multiple sub-graphs and placed in nodes in a distributed system. But querying these fragmented sub-graphs poses new challenges, such as increased communication costs, due to distributed joins involving cut edges. To combat these problems, a good partitioning should reduce the edge cuts while considering a given query workload. However, a partitioned graph needs to be continually re-partitioned to accommodate changes in the query workload and maintain a good average processing time. In this paper, an adaptive partitioning method for large-scale knowledge graphs is introduced, which adapts the partitioning in response to changes in the query workload. Our evaluation demonstrates that the performance of processing time for queries is improved after dynamically adapting the partitioning of knowledge graph triples.
