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作 者:王耀暖 江有福 吴巧玲 WANG Yao-nuan;JIANG You-fu;WU Qiao-ling(School of Information Engineering,Zhejiang Ocean University,Zhoushan 316022,Zhejiang Province,China)
机构地区:[1]浙江海洋大学信息工程学院,浙江舟山316022
出 处:《信息技术》2024年第6期36-41,共6页Information Technology
基 金:浙江省教育厅科研项目(Y202044755);浙江省大学生科技创新项目(2021R411025)。
摘 要:为满足海量数据下用户对URL链接进行缩短与查询性能的需求,降低生成的短链码之间碰撞概率与消减笛卡尔积操作,提出一种在海量数据场景下基于散列函数的字符串压缩算法。该算法满足用户一次输入的信息输出结果相同,以及对不同次的相同输入的信息输出结果不同的需求,采用随机因子与短链码中包含库表位信息来削减短链码生成冲突和海量数据下带来的笛卡尔积操作。实验结果表明,改进后的算法压缩耗时性能略微变慢但碰撞率明显降低,在查询性能方面提升78%~130%,并随着数据量增多变得越明显。In order to meet the user’s requirements for shortening URL links and query performance under massive data,and reduce the collision probability between generated short chain codes and reduce the Cartesian product operation,a Hash function-based string compression algorithm in the massive data scenario is proposed.The algorithm meets the requirements of the same information output results of users’input at one time and different information output results of the same input at different times.It uses random factors and the library epitope information contained in the short chain code to reduce the short chain code generation conflict and the Cartesian product operation caused by massive data.Experiment results show that the time-consuming performance of the improved compression algorithm is slightly slower,while the collision rate is significantly reduced,and the query performance is improved by 78%~130%,which becomes more obvious with the increase of the amount of data.
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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