Leveraging RDF-Based Data Structures for Optimized Traversal in Decentralized Query Systems

The vast, decentralized nature of web data presents significant obstacles for efficient querying, especially in environments where data sources are unindexed and distributed across the web. Traditional approaches, including Link Traversal Query Processing (LTQP), struggle with performance issues such as slow query execution and an overwhelming number of HTTP requests due to the lack of centralized indexing. This study proposes an innovative optimization technique that utilizes RDF-based data structures to enhance query efficiency in such decentralized contexts. By introducing a structure-aware indexing framework, we enable the query engine to better anticipate the relevant data sources for traversal, thus reducing unnecessary queries and streamlining the retrieval process. Our method uses explicit data structure mappings, referred to as shape-based indexes, which align with the inherent schema-like properties of RDF data. Early experimental evaluations demonstrate that this approach can significantly reduce query processing time by up to 75% and decrease the number of redundant resource accesses by over 90%. This work provides a new direction for improving the scalability and efficiency of querying in decentralized data systems, offering a viable alternative to the challenges posed by the traditional methods of data retrieval in the open web.