In this paper, we introduce three new implementations of non-native methods for storing RDF data. These methods named RDFSPO, RDFPC and RDFVP, are based respectively on the statement table, property table and vertical partitioning approaches. As important, we consider the issue of how to select the most relevant strategy for storing the RDF data depending on the dataset characteristics. For this, we investigate the balancing between two performance metrics, including load time and query response time. In this context, we provide an empirical comparative study between on one hand the three proposed methods, and on the other hand the proposed methods versus the existing ones by using various publicly available datasets. Finally, in order to further assess where the statistically significant differences appear between studied methods, we have performed a statistical analysis, based on the non-parametric Friedman test followed by a Nemenyi post-hoc test. The obtained results clearly show that the proposed RDFVP method achieves highly competitive computational performance against other state-of-the-art methods in terms of load time and query response time.

在本文中，我们介绍了存储 RDF 数据的非本地方法的三种新实现。RDFSPO、RDFPC 和 RDFVP 这些方法分别基于语句表、属性表和垂直分区方法。同样重要的是，我们考虑了如何根据数据集特征选择最相关的策略来存储 RDF 数据的问题。为此，我们研究了两个性能指标之间的平衡，包括加载时间和查询响应时间。在这种情况下，我们一方面提供了三种建议方法之间的实证比较研究，另一方面通过使用各种公开可用的数据集，提出的方法与现有方法之间的比较研究。最后，为了进一步评估研究方法之间出现统计显着差异的地方，我们基于非参数 Friedman 检验和 Nemenyi 事后检验进行了统计分析。获得的结果清楚地表明，所提出的 RDFVP 方法在加载时间和查询响应时间方面与其他最先进的方法相比，实现了极具竞争力的计算性能。