To counter societal and economic problems caused by data silos on the Web, efforts such as Solid strive to reclaim private data by storing it in permissioned documents over a large number of personal vaults across the Web. Building applications on top of such a decentralized Knowledge Graph involves significant technical challenges: centralized aggregation prior to query processing is excluded for legal reasons, and current federated querying techniques cannot handle this large scale of distribution at the expected performance. We propose an extension to Link Traversal Query Processing (LTQP) that incorporates structural properties within decentralized environments to tackle their unprecedented scale. In this article, we analyze the structural properties of the Solid decentralization ecosystem that are relevant for query execution, and provide the SolidBench benchmark to simulate Solid environments representatively. We introduce novel LTQP algorithms leveraging these structural properties, and evaluate their effectiveness. Our experiments indicate that these new algorithms obtain accurate results in the order of seconds for non-complex queries, which existing algorithms cannot achieve. Furthermore, we discuss limitations with respect to more complex queries. This work reveals that a traversal-based querying method using structural assumptions can be effective for large-scale decentralization, but that advances are needed in the area of query planning for LTQP to handle more complex queries. These insights open the door to query-driven decentralized applications, in which declarative queries shield developers from the inherent complexity of a decentralized landscape.

翻译：为应对Web数据孤岛引发的社会与经济问题，Solid等方案致力于将个人数据存储于分布在Web上的大量许可文档中，以重新掌控隐私数据。基于此类去中心化知识图谱构建应用面临重大技术挑战：出于法律原因，查询处理前无法进行集中聚合；而现有联邦查询技术也无法在预期性能下处理如此大规模的数据分布。我们提出对链路遍历查询处理（LTQP）的扩展方案，通过融入去中心化环境的结构特性来应对其前所未有的规模。本文分析了与查询执行相关的Solid去中心化生态系统结构特性，并提供了SolidBench基准测试以具代表性方式模拟Solid环境。我们提出了利用这些结构特性的新型LTQP算法，并评估了其有效性。实验表明，对于非复杂查询，这些新算法可在数秒内获得精准结果，而现有算法无法实现。此外，我们讨论了针对更复杂查询的局限性。本研究揭示，基于结构假设的遍历式查询方法可有效支持大规模去中心化场景，但LTQP需要推进查询规划领域的发展以应对更复杂查询。这些发现为查询驱动的去中心化应用打开了大门，在其中声明式查询能使开发者免于应对去中心化环境的内在复杂性。