基于大数据处理技术的界面交互设计研究
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摘要
为了提高界面的信息交互和数据处理能力,提出基于大数据处理技术的界面交互设计方法。构建界面交互系统的数据库,采用过程约束的层次式结构设计方法进行界面信息交互和大数据融合处理,采用模糊聚类方法进行界面检索数据库的信息聚类。在Linux内核源码控制下进行界面的程序调度和交叉编译,界面的交互设计系统主要包括进程管理、程序控制和内源文件管理等模块,结合大数据处理技术,实现界面交互系统优化设计。系统测试结果表明,设计的界面交互系统具有很好的大数据信息处理和调度能力,数据的召回性较好。
The interface interaction design method based on big data processing technology is put forward to improve the abilities of interface information interaction and data processing,and its database is constructed. The hierarchical structure design method with process constraints is used to perform the interface information interaction and big data fusion. The fuzzy clustering method is used to cluster the information of the interface retrieval database. The program scheduling and cross compiling of the interface are carried out under the control of Linux kernel source code. The interactive design system of the interface mainly includes the modules of process management,program control and internal source file management. The big data processing technology is combined in the system to realize the optimal design of interface interaction system. The system test results show that the designed interface interaction system has high big data information processing performance and strong scheduling ability,and perfect data recall rate.
The interface interaction design method based on big data processing technology is put forward to improve the abilities of interface information interaction and data processing,and its database is constructed. The hierarchical structure design method with process constraints is used to perform the interface information interaction and big data fusion. The fuzzy clustering method is used to cluster the information of the interface retrieval database. The program scheduling and cross compiling of the interface are carried out under the control of Linux kernel source code. The interactive design system of the interface mainly includes the modules of process management,program control and internal source file management. The big data processing technology is combined in the system to realize the optimal design of interface interaction system. The system test results show that the designed interface interaction system has high big data information processing performance and strong scheduling ability,and perfect data recall rate.
引文
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[6] ZHANG R,HO C K. MIMO broadcasting for simultaneous wireless information and power transfer[J]. IEEE transactions on wireless communications,2013,12(5):1989-2001.
[7] WANG Y,SUN R,WANG X. Transceiver design to maximize the weighted sum secrecy rate in full-duplex SWIPT systems[J]. IEEE signal processing letters,2016,23(6):883-887.
[8]王玲娣,徐华. AdaBoost的多样性分析及改进[J].计算机应用,2018,38(3):650-654.WANG Lingdi,XU Hua. Diversity analysis and improvement of AdaBoost[J]. Journal of computer applications,2018,38(3):650-654.
[9]李文辉,倪洪印.一种改进的AdaBoost训练算法[J].吉林大学学报(理学版),2011,49(3):498-504.LI W H,NI H Y. An improved AdaBoost training algorithm[J]. Journal of Jilin University(science edition),2011,49(3):498-504.
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[11] LI N,YU Y,ZHOU Z H. Diversity regularized ensemble pruning[C]//Proceedings of 2012 Joint European Conference on Machine Learning and Knowledge Discovery in Databases.Berlin:Springer,2012:330-345.
[12] PARVIN H,MIRNABIBABOLI M,ALINEJAD-ROKNY H.Proposing a classifier ensemble framework based on classifier selection and decision tree[J]. Engineering applications of artificial intelligence,2015,37:34-42.
[13]曹莹,苗启广,刘家辰,等.AdaBoost算法研究进展与展望[J].自动化学报,2013,39(6):745-758.CAO Y,MIAO Q G,LIU J C,et al. Advance and prospects of AdaBoost algorithm[J]. Acta automatica Sinica,2013,39(6):745-758.
[14]陆兴华,陈平华.基于定量递归联合熵特征重构的缓冲区流量预测算法[J].计算机科学,2015,42(4):68-71.LU Xinghua,CHEN Pinghua. Traffic prediction algorithm in buffer based on recurrence quantification union entropy feature reconstruction[J]. Computer science,2015,42(4):68-71.
[15] OKANDEJI A A,KHANDAKER M R A,WONG K K,et al.Joint transmit power and relay two-way beamforming optimization for energy-harvesting full-duplex communications[C]//2016 IEEE Globecom Workshops. Washington,DC:IEEE,2016:1-6.