Learned-Index-Based Semantic Keyword Query on Blockchain

Author:

Yao Zhongming1,Xin Junchang12ORCID,Hao Kun34,Wang Zhiqiong3ORCID,Zhu Wancheng5

Affiliation:

1. School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China

2. Key Laboratory of Big Data Management and Analytics (Liaoning Province), Shenyang 110819, China

3. College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China

4. Neusoft Corporation (Research Center of Liaoning Promotion for Blockchain Engineering Technology), Shenyang 110819, China

5. Center for Rock Instability and Seismicity Research, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China

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.

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities of China

Open Program of Neusoft Corporation, China

Publisher

MDPI AG

Subject

General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)

Reference29 articles.

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3. McConaghy, T., Marques, R., Müller, A., De Jonghe, D., McConaghy, T., McMullen, G., Henderson, R., Bellemare, S., and Granzotto, A. (2016). Bigchaindb: A Scalable Blockchain Database, BigChainDB. white paper.

4. Riegger, C., Vincon, T., and Petrov, I. (2014, January 4–6). Efficient Data and Indexing Structure for Blockchains in Enterprise Systems. Proceedings of the 20th International Conference on Information Integration and Web-Based Applications & Services, Assoc Comp Machinery, Hanoi, Vietnam.

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