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A distributed query execution engine of big attributed graphs.

Omar Batarfi1, Radwa Elshawi2, Ayman Fayoumi1

  • 1King Abdulaziz University, Jeddah, Saudi Arabia.

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Summary
This summary is machine-generated.

DG-SPARQL offers an efficient, distributed solution for querying large attributed graphs. This hybrid engine significantly outperforms existing systems like Apache Giraph in performance and scalability.

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Area of Science:

  • Computer Science
  • Data Management
  • Graph Databases

Background:

  • Attributed graphs are essential for modeling real-world relationships with descriptive data.
  • Centralized graph query engines like G-SPARQL face scalability limitations.
  • Efficient querying of massive attributed graph datasets is a significant challenge.

Purpose of the Study:

  • To design, implement, and evaluate DG-SPARQL, a distributed, hybrid, and adaptive parallel execution engine for G-SPARQL queries.
  • To address the scalability limitations of centralized attributed graph query systems.
  • To demonstrate the performance advantages of a distributed approach for complex graph queries.

Main Methods:

  • DG-SPARQL distributes graph topology across node memory and replicates graph data in relational stores.
  • Query execution involves a hybrid approach, utilizing SQL queries for relational data and memory-based traversals for graph structures.
  • The system employs adaptive parallel processing techniques for efficient query plan evaluation.

Main Results:

  • DG-SPARQL demonstrates high efficiency and scalability in querying massive attributed graph datasets.
  • Experimental evaluations show DG-SPARQL outperforming Apache Giraph by orders of magnitude.
  • The hybrid execution strategy effectively balances relational and graph-based data processing.

Conclusions:

  • DG-SPARQL provides a scalable and efficient solution for querying large attributed graphs.
  • The distributed, hybrid architecture overcomes the limitations of centralized systems.
  • This approach offers significant performance improvements over existing distributed graph processing systems.