基于可变粒度机会调度的网络大数据知识扩充算法
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摘要
为了满足网络大数据背景下,大数据传播的数据知识高精度要求和清除劣质数据干扰,基于粒度可变调整方案提出了机会调度的网络大数据知识扩充算法。在分析网络大数据特征基础上,通过自适应向量编码,捕捉网络大数据的异构特性,采用多阶反向传播将异构网络大数据归一化处理,再通过机会调度实现网络大数据实时传输。同时,基于网络大数据组成的知识工程系统分割细粒度大数据,将多维特征进行降维处理,使得知识粒度转变为已知,接着调整粒度动态特性,使得知识工程的大数据集具有线性特征和明确的几何特性,通过知识扩充提高知识获取精度。实验结果通过与基于细粒度的知识获取算法进行对比,证明了所提算法的网络数据传输的高可靠性、实时性和知识获取的高效率。
In order to meet the needs of the network under the background of big data,and eliminate inferior data interference data knowledge high precision requirements of large data transmission,this paper proposed variable size adjustment scheme based on the algorithm to expand the network of large data knowledge opportunistic scheduling. Based on the analysis of large data network characteristics,it normalized the adaptive vector encoding,capture the heterogeneous characteristics of large data network,using multi order back-propagation network of heterogeneous data,and then through the real-time transmission of large data network to achieve opportunistic scheduling. At the same time,the knowledge engineering system composed of network data segmentation of fine-grained big data based on the multidimensional feature dimension,the granularity of knowledge transformation was known,then adjusted the size of the dynamic characteristics,making big data set of knowledge engineering with linear characteristics and clear geometric characteristics,improved the accuracy of knowledge acquisition through knowledge expansion. The experimental results are compared with the algorithm based on fine grained knowledge acquisition,which proves the high reliability,real time and high efficiency of network data transmission.
In order to meet the needs of the network under the background of big data,and eliminate inferior data interference data knowledge high precision requirements of large data transmission,this paper proposed variable size adjustment scheme based on the algorithm to expand the network of large data knowledge opportunistic scheduling. Based on the analysis of large data network characteristics,it normalized the adaptive vector encoding,capture the heterogeneous characteristics of large data network,using multi order back-propagation network of heterogeneous data,and then through the real-time transmission of large data network to achieve opportunistic scheduling. At the same time,the knowledge engineering system composed of network data segmentation of fine-grained big data based on the multidimensional feature dimension,the granularity of knowledge transformation was known,then adjusted the size of the dynamic characteristics,making big data set of knowledge engineering with linear characteristics and clear geometric characteristics,improved the accuracy of knowledge acquisition through knowledge expansion. The experimental results are compared with the algorithm based on fine grained knowledge acquisition,which proves the high reliability,real time and high efficiency of network data transmission.
引文
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[2] Przulj N,Malod-Dognin N. Network analytics in the age of big data[J]. Science,2016,353(6295):123-124.
[3] Gao Sheng,Pang Huacai,Gallinari P,et al. A novel embeddingmethod for information diffusion prediction in social network big data[J]. IEEE Trans on Industrial Informatics,2017,13(4):2097-2105.
[4] Kim R,Lim H,Krishnamachari B. Prefetching-based data dissemi-nation in vehicular cloud systems[J]. IEEE Trans on VehicularTechnology,2016,65(1):292-306.
[5]康文文,李浩敏,汤超,等.面向复杂工程系统设计的云知识平台[J].系统工程与电子技术,2017,39(5):1078-1084.(KangWenwen,Li Haomin,Tang Chao,et al. Cloud knowledge platform forcomplex system design[J]. System Engineering and Electronics,2017,39(5):1078-1084.)
[6] Zheng Xiaolong,Wang Jiliang,Dong Wei,et al. Bulk data dissemi-nation in wireless sensor networks:analysis,implications and im-provement[J]. IEEE Trans on Computers,2016,65(5):1428-1439.
[7]王齐,钱宇华,李飞江.基于空间结构的符号数据仿射传播算法[J].模式识别与人工智能,2016,29(12):1132-1139.(WangQi,Qian Yuhua,Li Feijiang. Space structure based affinity propaga-tion algorithm for categorical data[J]. Pattern Recognition and Ar-tificial Intelligence,2016,29(12):1132-1139.)
[8] Liu Yan,Niu Di,Khabbazian M. Cooper:expedite batch data dis-semination in computer clusters with coded gossips[J]. IEEE Transon Parallel&Distributed Systems,2017,28(8):2204-2217.
[9] Liu Yang,Wu Hongyi,Xia Yuanqing,et al. Optimal online data dis-semination for resource constrained mobile opportunistic networks[J]. IEEE Trans on Vehicular Technology,2017,66(6):5301-5315.
[10]王练,梁申虎,彭代渊.多源多中继无线网络中基于随机线性网络编码的调度方案[J].电子与信息学报,2017,39(3):532-538.(Wang Lian,Liang Shenhu,Peng Daiyuan. Scheduling schemefor multi-source multi-relay wireless network based on random linearnetwork coding[J]. Journal of Electronics&Information Tech-nology,2017,39(3):532-538.)
[11] Moharir S,Krishnasamy S,Shakkottai S. Scheduling in densifiednetworks:algorithms and performance[J]. IEEE/ACM Trans onNetworking,2017,25(1):164-178.
[12]Domingo-Prieto M,Chang Tengfei,Vilajosana X,et al. DistributedPID-based scheduling for 6tisch networks[J]. IEEE Communica-tions Letters,2016,20(5):1006-1009.
[13]林珲,游兰.虚拟地理环境知识工程初探[J].地球信息科学学报,2015,17(12):1423-1430.(Lin Hui,You Lan. A tentativestudy on knowledge engineering for virtual geographic environments[J]. Journal of Geo-Information Science,2015,17(12):1423-1430.)
[14]García-Pealvo F J. Engineering contributions to a multicultural per-spective of the knowledge society[J]. IEEE Revista Iberoamerica-na De Tecnologias Del Aprendizaje,2015,10(1):17-18.
[15]Wu Xindong,Chen Huanhuan,Wu Gongqing,et al. Knowledge en-gineering with big data[J]. IEEE Intelligent Systems,2015,30(5):46-55.