jXBW: Fast Substructure Search in Large-Scale JSONL Datasets for Foundation Model Applications (2508.12536v1)

Abstract: Substructure search in JSON Lines (JSONL) datasets is essential for modern applications such as prompt engineering in foundation models, but existing methods suffer from prohibitive computational costs due to exhaustive tree traversal and subtree matching. We present jXBW, a fast method for substructure search on large-scale JSONL datasets. Our method makes three key technical contributions: (i) a merged tree representation built by merging trees of multiple JSON objects while preserving individual identities, (ii) a succinct data structure based on the eXtended Burrows-Wheeler Transform that enables efficient tree navigation and subpath search, and (iii) an efficient three-step substructure search algorithm that combines path decomposition, ancestor computation, and adaptive tree identifier collection to ensure correctness while avoiding exhaustive tree traversal. Experimental evaluation on real-world datasets demonstrates that jXBW consistently outperforms existing methods, achieving speedups of 16$\times$ for smaller datasets and up to 4,700$\times$ for larger datasets over tree-based approaches, and more than 6$\times$10$^6$ over XML-based processing while maintaining competitive memory usage.