A Multi-Level Index Construction Method for Master-Slave Blockchain

Wang Junlu; Zhang Guiyue; Du Likuan; Li Su; Chen Tingwei

doi:10.7544/issn1000-1239.202220739

Journal of Computer Research and Development > 2024 > 61(3): 799-807. > DOI: 10.7544/issn1000-1239.202220739 CSTR: 32373.14.issn1000-1239.202220739

Wang Junlu, Zhang Guiyue, Du Likuan, Li Su, Chen Tingwei. A Multi-Level Index Construction Method for Master-Slave Blockchain[J]. Journal of Computer Research and Development, 2024, 61(3): 799-807. DOI: 10.7544/issn1000-1239.202220739

Citation:

PDF (1304 KB)

A Multi-Level Index Construction Method for Master-Slave Blockchain

School of Information, Liaoning University, Shenyang 110036

Funds: This work was supported by the National Key Research and Development Program of China(2021YFF0901004), the Applied Basic Research Program of Liaoning Province (2022JH2/101300250), the Digital Liaoning Intelligent Manufacturing Strong Province Funds for Direction of Digital Economy(13031307053000568), the Central Government Guides Local Science and Technology Development Foundation Project of Liaoning Province (2022JH6/100100032), and the Natural Science Foundation of Liaoning Province (2022-KF-13-06).

More Information

Author Bio:
Wang Junlu: born in 1988. PhD, lecturer. Member of CCF. His main research interests include blockchain technology, big data processing technology, and streaming data processing technology

Zhang Guiyue: born in 1996. Master. Her main research interests include blockchain technology, big data processing technology, and machine learning

Du Likuan: born in 1999. Master candidate. His main research interests include blockchain technology, big data processing technology, and machine learning

Li Su: born in 1997. PhD candidate. Member of CCF. Her main research interests include blockchain technology, big data processing technology, and streaming data processing technology

Chen Tingwei: born in 1974. PhD, professor, master supervisor. Member of CCF. His main research interests include intelligent transportation and machine learning
Received Date: August 21, 2022
Revised Date: April 18, 2023
Available Online: November 29, 2023

Graphical Abstract

Abstract

Abstract

Master-slave blockchain is a novel information processing technology that is domain-oriented and uses efficient cryptography principles for trustworthy communication and storage of big data. With the exponential growth of the scale of domain data, the existing master-slave blockchain system has increasingly serious problems such as low query efficiency and long traceability time. To address these issues, we propose a multi-level index construction method for master-slave blockchain (MSMLI). Firstly, MSMLI introduces a weight matrix and partitions the entire master-slave blockchain based on the master chain structure, and the weight of each partition is assigned. Secondly, for the master blockchain in each partition, a master chain index construction method based on jump consistent Hash (JHMI) is proposed, which takes the key value of the nodes and the number of index slots as input and outputs the master chain index. Finally, a Bloom filter is introduced to improve the column-based selection function and a secondary composite index on the subordinate blockchain corresponding to each master block is built. Experimental results on three constraint conditions and two types of datasets demonstrate that the proposed method reduces the index construction time by an average of 9.28%, improves the query efficiency by 12.07%, and reduces the memory overhead by 24.4%.
- blockchain,
- indexing,
- sharding,
- jump consistency Hash,
- improved Bloom filter

FullText(HTML)

References (20)

References

[1]	Barbosa M, Barthe G, Bhargavan K, et al. SoK: Computer-aided cryptography [C] //Proc of the 42nd IEEE Symp on Security and Privacy (S&P). Piscataway, NJ: IEEE, 2021: 777-795
[2]	朱建明,张沁楠,高胜,等. 基于区块链的隐私保护可信联邦学习模型[J]. 计算机学报,2021,44(12):2464−2484 doi: 10.11897/SP.J.1016.2021.02464 Zhu Jianming, Zhang Qinnan, Gao Sheng, et al. Blockchain-based trusted federated learning model for privacy protection[J]. Chinese Journal of Computers, 2021, 44(12): 2464−2484(in Chinese) doi: 10.11897/SP.J.1016.2021.02464
[3]	Moudoud H, Cherkaoui S, Khoukhi L. Towards a scalable and trustworthy blockchain: IoT use case [C/OL] //Proc of IEEE Int Conf on Communications (ICC 2021). Piscataway, NJ: IEEE, 2021[2022-02-16].https://ieeexplore.ieee.org/document/9500535
[4]	Li Chenxing, Li Peilun, Zhou Dong, et al. A decentralized blockchain with high throughput and fast confirmation [C] //Proc of USENIX Annual Technical Conf (USENIX ATC 2020). Berkeley, CA: USENIX Association, 2020: 515−528
[5]	Połap D, Srivastava G, Jolfaei A, et al. Blockchain technology and neural networks for the Internet of medical things [C] //Proc of the 39th IEEE Conf on Computer Communications Workshops (INFOCOM WKSHPS). Piscataway, NJ: IEEE, 2020: 508−513
[6]	魏松杰,李莎莎,王佳贺. 基于身份密码系统和区块链的跨域认证协议[J]. 计算机学报,2021,44(5):908−920 doi: 10.11897/SP.J.1016.2021.00908 Wei Songjie, Li Shasha, Wang Jiahe. Cross-domain authentication protocols based on identity cryptosystems and blockchains[J]. Chinese Journal of Computers, 2021, 44(5): 908−920(in Chinese) doi: 10.11897/SP.J.1016.2021.00908
[7]	Bao Jiabin, He Debiao, Luo Min, et al. A survey of blockchain applications in the energy sector[J]. IEEE Systems Journal, 2020, 15(3): 3370−3381
[8]	Bhaskar P, Tiwari C K, Joshi A. Blockchain in education management: Present and future applications [J/OL]. Interactive Technology and Smart Education, 2020[2022-03-21]. https://www.emerald.com/insight/content/doi/10.1108/ITSE-07-2020-0102/full/html
[9]	Kalodner H, Möser M, Lee K, et al. BlockSci: Design and applications of a blockchain analysis platform [C] //Proc of the 29th USENIX Security Symp (USENIX Security 2020). Berkeley, CA: USENIX Association, 2020: 2721-2738
[10]	Alladi T, Chamola V, Sahu N, et al. Applications of blockchain in unmanned aerial vehicles: A review [J/OL]. Vehicular Communications, 2020[2021-12-16].https://www.sciencedirect.com/science/article/abs/pii/S2214209620300206
[11]	Huang Junqin, Kong Linghe, Chen Guihai, et al. Towards secure industrial IoT: Blockchain system with credit-based consensus mechanism[J]. IEEE Transactions on Industrial Informatics, 2019, 15(6): 3680−3689 doi: 10.1109/TII.2019.2903342
[12]	Tsang Y, Choy K, Wu C, et al. Blockchain-driven IoT for food traceability with an integrated consensus mechanism[J]. IEEE Access, 2019, 7: 129000−129017 doi: 10.1109/ACCESS.2019.2940227
[13]	隋源,汪卫,邓雪. 一种面向区块链的链下数据库高吞吐量可验证查询方法[J]. 小型微型计算机系统,2021,42(6):1304−1312 doi: 10.3969/j.issn.1000-1220.2021.06.028 Sui Yuan, Wang Wei, Deng Xue. High throughput verifiable query method for blockchain-oriented off-chain database[J]. Journal of Chinese Computer Systems, 2021, 42(6): 1304−1312(in Chinese) doi: 10.3969/j.issn.1000-1220.2021.06.028
[14]	蔡磊,朱燕超,郭庆兴,等. 面向区块链的高效物化视图维护和可信查询[J]. 软件学报,2020,31(3):680−694 Cai Lei, Zhu Yanchao, Guo Qingxing, et al. Efficient materialized view maintenance and trusted query for blockchain[J]. Journal of Software, 2020, 31(3): 680−694(in Chinese)
[15]	Nathan V, Ding J, Alizadeh M, et al. Learning multi-dimensional indexes [C]//Proc of ACM SIGMOD Int Conf on Management of Data. New York: ACM, 2020: 985−1000
[16]	Huang Xiaoju, Gong Xueqing, Huang ZhiGang, et al. EBTree: A B-plus tree based index for ethereum blockchain data [C] //Proc of Asia Service Sciences and Software Engineering Conf (ASSE 2020). New York: ACM, 2020: 83−90
[17]	Kipf A, Marcus R, van Renen A, et al. RadixSpline: A single-pass learned index [C/OL] //Proc of the 3rd Int Workshop on Exploiting Artificial Intelligence Techniques for Data Management (aiDM 2020). New York: ACM, 2020[2022-02-15]. https://dl.acm.org/doi/10.1145/3401071.3401659
[18]	Xing Xiaogang, Chen Yuling, Li Tao, et al. A blockchain index structure based on subchain query[J]. Journal of Cloud Computing, 2021, 10(1): 1−11 doi: 10.1186/s13677-020-00210-w
[19]	Alghamdi N, Zhang Liang, Zhang Huayi, et al. ChainLink: Indexing big time series data for long subsequence matching [C] //Proc of the 36th IEEE Int Conf on Data Engineering (ICDE). Piscataway, NJ: IEEE, 2020: 529−540
[20]	高远宁,叶金标,杨念祖,等. 基于中间层的可扩展学习索引技术[J]. 软件学报,2020,31(3):620−633 Gao Yuanning, Ye Jinbiao, Yang Nianzu, et al. Middle layer based scalable learned index scheme[J]. Journal of Software, 2020, 31(3): 620−633(in Chinese)

[1]	Xu Ke, Li Yanbiao, Xie Gaogang, Zhang Dafang. Efficient Name Lookup Method Based on Hybrid Counting Bloom Filters[J]. Journal of Computer Research and Development, 2023, 60(5): 1136-1150. DOI: 10.7544/issn1000-1239.202111242
[2]	Jiang Gaoxia, Wang Wenjian. A Numerical Label Noise Filtering Algorithm for Regression Task[J]. Journal of Computer Research and Development, 2022, 59(8): 1639-1652. DOI: 10.7544/issn1000-1239.20220053
[3]	Wei Zheng, Dou Yu, Gao Yanzhen, Ma Jie, Sun Ninghui, Xing Jing. A Consistent Hash Data Placement Algorithm Based on Stripe[J]. Journal of Computer Research and Development, 2021, 58(4): 888-903. DOI: 10.7544/issn1000-1239.2021.20190732
[4]	Xie Qin, Zhang Qinghua, Wang Guoyin. An Adaptive Three-way Spam Filter with Similarity Measure[J]. Journal of Computer Research and Development, 2019, 56(11): 2410-2423. DOI: 10.7544/issn1000-1239.2019.20180793
[5]	Meng Huanyu, Liu Zhen, Wang Fang, Xu Jiadong, Zhang Guoqiang. An Efficient Collaborative Filtering Algorithm Based on Graph Model and Improved KNN[J]. Journal of Computer Research and Development, 2017, 54(7): 1426-1438. DOI: 10.7544/issn1000-1239.2017.20160302
[6]	Li Wei, Zhang Dafang, Xie Kun, Li Wenwei, He Jie. A Matrix-Indexed Bloom Filter for Flash-Based Key-Value Store[J]. Journal of Computer Research and Development, 2015, 52(5): 1210-1222. DOI: 10.7544/issn1000-1239.2015.20131940
[7]	Zhang Lunkai, Song Fenglong, Wang Da, Fan Dongrui, Sun Ninghui. Improving the Performance of Sparse Directories[J]. Journal of Computer Research and Development, 2014, 51(9): 1955-1970. DOI: 10.7544/issn1000-1239.2014.20131173
[8]	Wang Ling, Liu Shanjun, Chen Binglin, Ji Changying. Heuristic Discrimination Cotton Ripeness Using Hybrid Filter and Wrapper[J]. Journal of Computer Research and Development, 2013, 50(2): 269-277.
[9]	Wang Li, Xu Mingwei, and Xu Ke. On the Deployment Approach of IPSec and IP Filter in Routers[J]. Journal of Computer Research and Development, 2006, 43(3): 375-380.
[10]	Ma Liang, Chen Qunxiu, and Cai Lianhong. An Improved Model for Adaptive Text Information Filtering[J]. Journal of Computer Research and Development, 2005, 42(1): 79-84.