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Multi-level index construction method based on master-slave blockchains.

Haolin Zhang1, Su Li1, Chen Liu1

  • 1School of Information, Liaoning University, Shenyang, 110036, China.

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|February 20, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a multi-level index construction method (MLI) to enhance master-slave blockchain performance. MLI significantly reduces index construction time, improves query efficiency, and lowers memory usage for big data processing.

Keywords:
BlockchainImproved bloom filterIndexingJump consistency hashingSharding

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Area of Science:

  • Computer Science
  • Data Science
  • Information Technology

Background:

  • Master-slave blockchain systems are crucial for big data processing but face scalability challenges.
  • Existing indexing methods struggle with large datasets, leading to low query efficiency and long traceback times.
  • Exponential data growth exacerbates performance issues in current master-slave blockchain architectures.

Purpose of the Study:

  • To propose a novel multi-level index construction method (MLI) for master-slave blockchains.
  • To address the limitations of existing indexing techniques in handling large-scale domain data.
  • To improve the overall efficiency and reduce the resource overhead of master-slave blockchain systems.

Main Methods:

  • Introduced a weight matrix for partitioning the master-slave blockchain and assigning partition weights.
  • Developed a jump consistent hash-based master chain index construction method (JHMI).
  • Integrated a bloom filter to create a secondary composite index for subordinate blockchains.

Main Results:

  • MLI reduced index construction time by an average of 9.28%.
  • Query efficiency was improved by an average of 12.07%.
  • Memory overhead was reduced by an average of 24.4% across various datasets and conditions.

Conclusions:

  • The proposed multi-level index construction method (MLI) effectively enhances master-slave blockchain performance.
  • MLI offers a viable solution for optimizing big data processing in domain-oriented blockchain systems.
  • The method demonstrates significant improvements in efficiency and resource utilization compared to existing approaches.