gRDF: An Efficient Compressor with Reduced Structural Regularities That Utilizes gRePair

The explosive volume of semantic data published in the Resource Description Framework (RDF) data model demands efficient management and compression with better compression ratio and runtime. Although extensive work has been carried out for compressing the RDF datasets, they do not perform well in all dimensions. However, these compressors rarely exploit the graph patterns and structural regularities of real-world datasets. Moreover, there are a variety of existing approaches that reduce the size of a graph by using a grammar-based graph compression algorithm. In this study, we introduce a novel approach named gRDF (graph repair for RDF) that uses gRePair, one of the most efficient grammar-based graph compression schemes, to compress the RDF dataset. In addition to that, we have improved the performance of HDT (header-dictionary-triple), an efficient approach for compressing the RDF datasets based on structural properties, by introducing modified HDT (M-HDT). It can detect the frequent graph pattern by employing the data-structure-oriented approach in a single pass from the dataset. In our proposed system, we use M-HDT for indexing the nodes and edge labels. Then, we employ gRePair algorithm for identifying the grammar from the RDF graph. Afterward, the system improves the performance of k2-trees by introducing a more efficient algorithm to create the trees and serialize the RDF datasets. Our experiments affirm that the proposed gRDF scheme can substantially achieve at approximately 26.12%, 13.68%, 6.81%, 2.38%, and 12.76% better compression ratio when compared with the most prominent state-of-the-art schemes such as HDT, HDT++, k2-trees, RDF-TR, and gRePair in the case of real-world datasets. Moreover, the processing efficiency of our proposed scheme also outperforms others.