论文标题
自适应的低水平存储非常大的知识图
Adaptive Low-level Storage of Very Large Knowledge Graphs
论文作者
论文摘要
Web上知识图（kgs）的可用性和使用的增加，要求可扩展和通用解决方案来存储这种类型的数据结构。我们提出了Trident，这是一种新颖的存储架构，用于集中式系统的非常大的公斤。 Trident使用几个相互关联的数据结构来快速访问节点和边缘，并根据图表的拓扑而更改物理存储，以减少内存足迹。与专为单个任务设计的单个体系结构相反，我们的方法提供了一个界面，具有少数低级和通用的原始原始图，可用于实现SPARQL查询响应，推理或图形分析等任务。我们的实验表明，Trident可以使用廉价的硬件处理10^11个边缘的图形，从而在多个工作负载上提供竞争性能。
The increasing availability and usage of Knowledge Graphs (KGs) on the Web calls for scalable and general-purpose solutions to store this type of data structures. We propose Trident, a novel storage architecture for very large KGs on centralized systems. Trident uses several interlinked data structures to provide fast access to nodes and edges, with the physical storage changing depending on the topology of the graph to reduce the memory footprint. In contrast to single architectures designed for single tasks, our approach offers an interface with few low-level and general-purpose primitives that can be used to implement tasks like SPARQL query answering, reasoning, or graph analytics. Our experiments show that Trident can handle graphs with 10^11 edges using inexpensive hardware, delivering competitive performance on multiple workloads.
