Hierarchical Characteristic Set Merging for Optimizing SPARQL Queries in Heterogeneous RDF

TL;DR

This paper introduces a hierarchical merging technique for characteristic sets in RDF data, reducing schema heterogeneity effects and improving storage and query performance in SPARQL processing.

Contribution

It proposes a novel hierarchy-based merging method for characteristic sets, effectively managing schema heterogeneity in RDF datasets.

Findings

Significant reduction in the number of characteristic sets.

Improved query performance and storage efficiency.

Effective handling of schema heterogeneity in RDF data.

Abstract

Characteristic sets (CS) organize RDF triples based on the set of properties characterizing their subject nodes. This concept is recently used in indexing techniques, as it can capture the implicit schema of RDF data. While most CS-based approaches yield significant improvements in space and query performance, they fail to perform well in the presence of schema heterogeneity, i.e., when the number of CSs becomes very large, resulting in a highly partitioned data organization. In this paper, we address this problem by introducing a novel technique, for merging CSs based on their hierarchical structure. Our technique employs a lattice to capture the hierarchical relationships between CSs, identifies dense CSs and merges dense CSs with their ancestors, thus reducing the size of the CSs as well as the links between them. We implemented our algorithm on top of a relational backbone, where each merged CS is stored in a relational table, and we performed an extensive experimental study to evaluate the performance and impact of merging to the storage and querying of RDF datasets, indicating significant improvements.