Efficient Query Model for Storage Capacity Scalable Blockchain System

被引：0
作者：
Jia D.-Y. [1 ]
Xin J.-C. [1 ,2 ]
Wang Z.-Q. [3 ]
Guo W. [4 ]
Wang G.-R. [5 ]
机构：
[1] School of Computer Science and Engineering, Northeastern University, Shenyang
[2] Liaoning Provincial Key Laboratory of Big Data Management and Analytics, Shenyang
[3] Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang
[4] School of Computer, Shenyang Aerospace University, Shenyang
[5] School of Computer Science and Technology, Beijing Institute of Technology, Beijing
来源：
Ruan Jian Xue Bao/Journal of Software | 2019年 / 30卷 / 09期
基金：
中国博士后科学基金; 中国国家自然科学基金;
关键词：
B-M tree; Blockchain; ElasticQM; Query algorithm; Scalable capacity;
D O I：
10.13328/j.cnki.jos.005774
中图分类号：
学科分类号：
摘要：
Blockchain technology is a research hotspot in the field of computers today. The decentralized and secured blockchain data effectively reduces the trust costs of the real economy. This study proposes an efficient query method for the scalable model of blockchain storage capacity-ElasticQM. The ElasticQM query model consists of four layers of modules: user layer, query layer, storage layer, and data layer. The user layer model puts the query results into the cache, which speeds up the query speed when querying the same data again. In the query level, this study proposes a global query optimization algorithm for the scalable blockchain model, which increases the roles of querying super nodes, query verification nodes and querying leaf nodes. It improves the efficiency of global queries. In the storage layer, the model improves the data storage process of the ElasticChain, which supports large scale blockchain. The storage layer achieves the scalability of the blockchain's capacity and reduces the storage space. In the data layer, this study proposes a blockchain storage structure based on B-M tree, and gives the establishment algorithm of B-M tree and search algorithm based on B-M tree. Blockchains based on B-M trees will increase the speed of queries in local search within a block. The experimental results on real datasets show that the ElasticQM model has efficient query efficiency. © Copyright 2019, Institute of Software, the Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码：2655 / 2670
页数：15
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