Learned-Index-Based Semantic Keyword Query on Blockchain

Zhongming Yao, Junchang Xin (), Kun Hao, Zhiqiong Wang and Wancheng Zhu
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Zhongming Yao: School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
Junchang Xin: School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
Kun Hao: College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China
Zhiqiong Wang: College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China
Wancheng Zhu: Center for Rock Instability and Seismicity Research, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China

Mathematics, 2023, vol. 11, issue 9, 1-19

Abstract: Blockchain has become increasingly popular for data management in recent years. However, the existing blockchain systems lack efficient semantic queries, particularly keyword queries. To address this issue, we propose a learned-index-based semantic keyword query architecture on blockchain. First, our architecture records data semantics information to support semantic keyword queries. Second, we establish the lookup table index for semantic information among blocks and the block-level recursive model index for blocks to improve the query efficiency. We store the lookup table in the extended block headers to maintain the result’s completeness, and we store recursive model indexes off chain to optimize the maintenance efficiency. Third, we propose a verifiable query algorithm based on our proposed architecture to maintain the result’s correctness. Finally, the experimental results show that combining the lookup table and the learned index effectively improves the query efficiency on blockchain.

Keywords: blockchain; learned index; keyword query; semantic query (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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