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

Tangina Sultana1, Young-Koo Lee1

  • 1Department of Computer Science and Engineering, Kyung Hee University, Global Campus, Yongin-si 17104, Korea.

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PubMed
Summary
This summary is machine-generated.

This study introduces gRDF, a novel approach for compressing Resource Description Framework (RDF) data. gRDF significantly improves compression ratios and processing efficiency for semantic datasets by leveraging graph patterns.

Keywords:
RDFcompressiongRePairgraphk2-trees

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Area of Science:

  • Computer Science
  • Data Compression
  • Semantic Web Technologies

Background:

  • The Resource Description Framework (RDF) data model is experiencing rapid growth, necessitating efficient data management and compression techniques.
  • Existing RDF compression methods often fail to exploit the inherent graph patterns and structural regularities of real-world datasets, limiting their effectiveness.
  • Grammar-based graph compression algorithms offer potential but require further optimization for RDF datasets.

Purpose of the Study:

  • To develop a novel compression scheme, gRDF, for Resource Description Framework (RDF) datasets that enhances compression ratio and processing efficiency.
  • To improve upon existing compression techniques by effectively utilizing graph patterns and structural properties of RDF data.
  • To evaluate the performance of the proposed gRDF scheme against state-of-the-art RDF compression methods.

Main Methods:

  • Introduced gRDF, a novel approach combining the gRePair grammar-based compression scheme with an improved Header-Dictionary-Triple (HDT) structure called M-HDT.
  • Utilized M-HDT for efficient single-pass indexing of nodes and edge labels, detecting frequent graph patterns.
  • Employed the gRePair algorithm to identify grammars from RDF graphs and enhanced k2-trees for serialization.

Main Results:

  • The gRDF scheme achieved superior compression ratios compared to prominent methods: HDT (26.12%), HDT++ (13.68%), k2-trees (6.81%), RDF-TR (2.38%), and gRePair (12.76%).
  • Experimental results demonstrate substantial improvements in compression ratio across various real-world RDF datasets.
  • The proposed gRDF scheme also exhibited enhanced processing efficiency over existing approaches.

Conclusions:

  • gRDF offers a significant advancement in RDF data compression, outperforming current state-of-the-art methods in both compression ratio and processing speed.
  • The integration of grammar-based compression with optimized structural indexing provides an effective strategy for managing large-scale semantic data.
  • This research contributes to more efficient storage and retrieval of information within the Resource Description Framework ecosystem.