Data on the web is naturally unindexed and decentralized. Centralizing web data, especially personal data, raises ethical and legal concerns. Yet, compared to centralized query approaches, decentralization-friendly alternatives such as Link Traversal Query Processing (LTQP) are significantly less performant and understood. The two main difficulties of LTQP are the lack of apriori information about data sources and the high number of HTTP requests. Exploring decentralized-friendly ways to document unindexed networks of data sources could lead to solutions to alleviate those difficulties. RDF data shapes are widely used to validate linked data documents, therefore, it is worthwhile to investigate their potential for LTQP optimization. In our work, we built an early version of a source selection algorithm for LTQP using RDF data shape mappings with linked data documents and measured its performance in a realistic setup. In this article, we present our algorithm and early results, thus, opening opportunities for further research for shape-based optimization of link traversal queries. Our initial experiments show that with little maintenance and work from the server, our method can reduce up to 80% the execution time and 97% the number of links traversed during realistic queries. Given our early results and the descriptive power of RDF data shapes it would be worthwhile to investigate non-heuristic-based query planning using RDF shapes.

翻译：网络数据天然具有未索引和去中心化的特性。将网络数据（特别是个人数据）中心化会引发伦理与法律问题。然而，与中心化查询方法相比，去中心化友好的替代方案（如链接遍历查询处理）在性能和认知度方面明显不足。链接遍历查询面临两大主要困难：一是缺乏关于数据源的先验信息，二是HTTP请求数量庞大。探索去中心化友好的方式来记录未索引数据源网络，可能为解决这些难题提供途径。RDF数据形状被广泛用于验证关联数据文档，因此研究其在链接遍历查询优化中的潜力具有重要价值。在本研究中，我们利用RDF数据形状映射与关联数据文档，构建了链接遍历查询的源选择算法早期版本，并在真实场景中评估了其性能。本文介绍了该算法及初步实验结果，从而为基于形状的链接遍历查询优化开辟了进一步的研究方向。初步实验表明：在服务器只需少量维护工作的情况下，我们的方法能在实际查询中将执行时间减少最高80%，遍历链接数量减少最高97%。基于这些早期成果及RDF数据形状的描述能力，未来值得研究基于RDF形状的非启发式查询规划方法。