论文标题
SymphonyDB：用于知识图查询处理的多面体模型
SymphonyDB: A Polyglot Model for Knowledge Graph Query Processing
论文作者
论文摘要
解锁知识图（kgs）的全部潜力以启用或增强各种语义和其他应用程序，需要数据管理系统（DMS）有效地存储和处理kgs的内容。但是，在当前一代的DMS构成了kg查询的大小和种类的增加以及kg查询的不断增长，以至于代表性DMS的性能往往在不同的查询类型中往往差异很大，并且没有单个平台占主导地位。 We present our extensible prototype, SymphonyDB, as an approach to addressing this problem based on a polyglot model of query processing as part of a multi-database system supported by a unified access layer that can analyze/translate individual queries just-in-time and match each to the likely best-performing DMS among Virtuoso, Blazegraph, RDF-3X, and MongoDB as representative DMSs that are included in our目前原型。我们在众所周知的KG基准数据集上使用原型的实验结果表明，其性能在不同的查询类型和数据集中的效率和一致性。
Unlocking the full potential of Knowledge Graphs (KGs) to enable or enhance various semantic and other applications requires Data Management Systems (DMSs) to efficiently store and process the content of KGs. However, the increases in the size and variety of KG datasets as well as the growing diversity of KG queries pose efficiency challenges for the current generation of DMSs to the extent that the performance of representative DMSs tends to vary significantly across diverse query types and no single platform dominates performance. We present our extensible prototype, SymphonyDB, as an approach to addressing this problem based on a polyglot model of query processing as part of a multi-database system supported by a unified access layer that can analyze/translate individual queries just-in-time and match each to the likely best-performing DMS among Virtuoso, Blazegraph, RDF-3X, and MongoDB as representative DMSs that are included in our prototype at this time. The results of our experiments with the prototype over well-known KG benchmark datasets and queries point to the efficiency and consistency of its performance across different query types and datasets.
