The recent advancements of the Semantic Web and Linked Data have changed the working of the traditional web. There is significant adoption of the Resource Description Framework (RDF) format for saving of web-based data. This massive adoption has paved the way for the development of various centralized and distributed RDF processing engines. These engines employ various mechanisms to implement critical components of the query processing engines such as data storage, indexing, language support, and query execution. All these components govern how queries are executed and can have a substantial effect on the query runtime. For example, the storage of RDF data in various ways significantly affects the data storage space required and the query runtime performance. The type of indexing approach used in RDF engines is critical for fast data lookup. The type of the underlying querying language (e.g., SPARQL or SQL) used for query execution is a crucial optimization component of the RDF storage solutions. Finally, query execution involving different join orders significantly affects the query response time. This paper provides a comprehensive review of centralized and distributed RDF engines in terms of storage, indexing, language support, and query execution.

中文翻译：

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集中式和分布式 RDF 引擎中的存储、索引、查询处理和基准测试：调查

语义网和关联数据的最新进展改变了传统网络的工作方式。大量采用资源描述框架 (RDF) 格式来保存基于 Web 的数据。这种大规模采用为各种集中式和分布式 RDF 处理引擎的开发铺平了道路。这些引擎采用各种机制来实现查询处理引擎的关键组件，例如数据存储、索引、语言支持和查询执行。所有这些组件控制查询的执行方式，并且可以对查询运行时产生重大影响。例如，RDF 数据以各种方式的存储会显着影响所需的数据存储空间和查询运行时性能。RDF 引擎中使用的索引方法类型对于快速数据查找至关重要。用于查询执行的底层查询语言（例如，SPARQL 或 SQL）的类型是 RDF 存储解决方案的关键优化组件。最后，涉及不同连接顺序的查询执行会显着影响查询响应时间。本文在存储、索引、语言支持和查询执行方面对集中式和分布式 RDF 引擎进行了全面回顾。