使用柏林 SPARQL 基准评估关系数据库上的虚拟 SPARQL 端点

IF 2.7 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Milos Chaloupka, Martin Necasky
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引用次数: 0

摘要

RDF 是一种在网络上发布结构化数据的流行且文档齐全的格式。它能让数据在不知道数据内部存储方式的情况下被消费。目前已经有几种本地 RDF 存储解决方案提供 SPARQL 端点。然而,本地 RDF 存储并没有被广泛采用。将数据存储在关系数据库中仍然更为常见。原生 RDF 存储解决方案的一个有用功能是提供 SPARQL 端点,即使用 SPARQL 查询 RDF 数据的网络服务。为了在流行的关系数据库之上也能提供这一功能,出现了在关系数据库之上提供虚拟 SPARQL 端点的解决方案。为了对这些解决方案进行基准测试,我们使用了最先进的工具--柏林 SPARQL 基准(BSBM)。不过,BSBM 主要是为本地 RDF 存储基准而设计的。它也可用于对虚拟 SPARQL 端点的解决方案进行基准测试。但是,由于 BSBM 不是为虚拟 SPARQL 端点设计的,因此每个实施方案使用该工具进行评估的方式都不同。因此,评估结果并不一致,很难进行比较。在本文中,我们将演示如何使用这一定义明确的 SPARQL 端点基准测试工具来评估关系数据库上的虚拟端点,对可用的实现进行评估,并就将来如何重复相同的评估提供指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Berlin SPARQL benchmark to evaluate virtual SPARQL endpoints over relational databases

The RDF is a popular and well-documented format for publishing structured data on the web. It enables data to be consumed without the knowledge of how the data is internally stored. There are already several native RDF storage solutions that provide a SPARQL endpoint. However, native RDF stores are not widely adopted. It is still more common to store data in a relational database. One of the useful features of native RDF storage solutions is providing a SPARQL endpoint, a web service to query RDF data with SPARQL. To provide this feature also on top of prevalent relational databases, solutions for virtual SPARQL endpoints on top of a relational database have appeared. To benchmark these solutions, a state-of-the-art tool, the Berlin SPARQL Benchmark (BSBM), is used. However, BSBM was designed primarily to benchmark native RDF stores. It can also be used to benchmark solutions for virtual SPARQL endpoints. However, since BSBM was not designed for virtual SPARQL endpoints, each implementation uses that tool differently for evaluation. As a result, the evaluation is not consistent and therefore hardly comparable. In this paper, we demonstrate how this well-defined benchmarking tool for SPARQL endpoints can be used to evaluate virtual endpoints over relational databases, perform the evaluation on the available implementations, and provide instructions on how to repeat the same evaluation in the future.

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来源期刊
Data & Knowledge Engineering
Data & Knowledge Engineering 工程技术-计算机:人工智能
CiteScore
5.00
自引率
0.00%
发文量
66
审稿时长
6 months
期刊介绍: Data & Knowledge Engineering (DKE) stimulates the exchange of ideas and interaction between these two related fields of interest. DKE reaches a world-wide audience of researchers, designers, managers and users. The major aim of the journal is to identify, investigate and analyze the underlying principles in the design and effective use of these systems.
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