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Reducing vertices in property graphs.

Dominik Tomaszuk1, Karol Pąk1

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This study introduces a novel graph database approach to reduce data redundancy by grouping similar vertices and distant data clusters. This method enhances database performance by minimizing repetitive data, improving access speeds.

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Area of Science:

  • Computer Science
  • Database Management
  • Graph Theory

Background:

  • Graph databases experience significant growth, leading to data redundancy and performance issues.
  • Repetitive or similar data, particularly in large graph databases, slows down resource access.

Purpose of the Study:

  • To present a modified approach for managing graph databases by reducing data redundancy.
  • To group vertices with identical properties and merge data clusters from distant graph locations.

Main Methods:

  • Grouping vertices based on identical properties.
  • Developing a method to join data clusters located in distant graph parts.
  • Addressing the NP-hard problem of partitioning graphs into pairwise distant vertex sets using heuristics.

Main Results:

  • Demonstrated the NP-hard nature of partitioning graphs into sets with pairwise distant vertices.
  • Developed and applied heuristics to solve the computational challenges of data merging.
  • Showcased the effectiveness of the proposed approach in reducing data redundancy and improving database performance.

Conclusions:

  • The proposed method effectively reduces data redundancy in graph databases.
  • Heuristics provide a viable solution for the NP-hard problem of partitioning distant graph data.
  • Optimizing graph database structure through data consolidation significantly enhances access speeds.