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作 者:马新建 景翔 胡颖聪 朱晓旻 蔡华谦 林兵 孙艳春 MA Xinjian;JING Xiang;HU Yingcong;ZHU Xiaomin;CAI Huaqian;LIN Bing;SUN Yanchun(Advanced Institute of Big Data Beijing,Beijing 100195,China;School of Software&Microelectronics,Peking University,Beijing 102600,China;Beijing Fusion Galaxy Technology Co.,Ltd,Beijing 100080,China;School of Computer Science,Peking University,Beijing 100871,China;College of Physics and Energy,Fujian Normal University,Fuzhou 350117,China;National Key Laboratory of Data Space Technology and System,Beijing 100195,China)
机构地区:[1]北京大数据先进技术研究院,北京100195 [2]北京大学软件与微电子学院,北京102600 [3]北京智融云河科技有限公司,北京100080 [4]北京大学计算机学院,北京100871 [5]福建师范大学物理与能源学院,福州350117 [6]数据空间技术与系统全国重点实验室,北京100195
出 处:《小型微型计算机系统》2024年第10期2492-2499,共8页Journal of Chinese Computer Systems
基 金:国家重点研发计划项目(2020YFB1006001)资助.
摘 要:存证是分布式账本的重要应用领域.为了满足大数据场景下海量数据的存证需求,出现了新型的非全网共识账本.这种账本采用非全网共识算法,大大提高了系统的存证性能.但是,这种账本的数据会随机分布在网络中,因此对数据查询带来了挑战.为适应非全网共识账本数据分散存储的特点,提出了一种高效的数据查询方法.该方法在查询过程中加入了学习机制,通过账户地址的相似度来指导请求转发,使得查询过程具有更高的性能和更低的延迟.基于该方法,设计和实现了用于非全网共识账本的查询系统,并在仿真环境和真实环境中进行了测试.结果表明,该查询方法在查询性能方面提升了12%,在响应时间方面降低了12%,验证了该查询方法的有效性.Evidence Preservation is an important application field of distributed ledgers.In order to meet the evidence preservation requirements of massive data in the big data scenario,a new type of non-network consensus ledger has emerged.This ledger adopts a non-network-wide consensus algorithm,which greatly improved its performance.However,the data of this kind of ledger will be randomly distributed in the network,which brings challenge to data query.To adapt to the characteristics of decentralized storage of non-network consensus ledger data,an efficient data query method was proposed.This method adds learning mechanism to the query process and guides request forwarding through account address similarity,making the query process have better performance and lower latency.Based on this method,a query system for non-network consensus ledgers was designed and implemented and tested in both simulated and real environments.The results showed that the query method improves query performance by 12%,reduces response time by 12%,which verified the effectiveness of the query method.
分 类 号:TP311[自动化与计算机技术—计算机软件与理论]
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