In the data retrieval process of the Data recommendation system,the matching prediction and similarity identification take place a major role in the ontology.In that,there are several methods to improve the retrieving...In the data retrieval process of the Data recommendation system,the matching prediction and similarity identification take place a major role in the ontology.In that,there are several methods to improve the retrieving process with improved accuracy and to reduce the searching time.Since,in the data recommendation system,this type of data searching becomes complex to search for the best matching for given query data and fails in the accuracy of the query recommendation process.To improve the performance of data validation,this paper proposed a novel model of data similarity estimation and clustering method to retrieve the relevant data with the best matching in the big data processing.In this paper advanced model of the Logarithmic Directionality Texture Pattern(LDTP)method with a Metaheuristic Pattern Searching(MPS)system was used to estimate the similarity between the query data in the entire database.The overall work was implemented for the application of the data recommendation process.These are all indexed and grouped as a cluster to form a paged format of database structure which can reduce the computation time while at the searching period.Also,with the help of a neural network,the relevancies of feature attributes in the database are predicted,and the matching index was sorted to provide the recommended data for given query data.This was achieved by using the Distributional Recurrent Neural Network(DRNN).This is an enhanced model of Neural Network technology to find the relevancy based on the correlation factor of the feature set.The training process of the DRNN classifier was carried out by estimating the correlation factor of the attributes of the dataset.These are formed as clusters and paged with proper indexing based on the MPS parameter of similarity metric.The overall performance of the proposed work can be evaluated by varying the size of the training database by 60%,70%,and 80%.The parameters that are considered for performance analysis are Precision,Recall,F1-score and the accuracy of data retrieval,the query recommendation output,and comparison with other state-of-art methods.展开更多
文章以1996-2015年Web of Science数据库收录的大数据领域文献为研究样本,从总体态势、空间格局演变特征和当前研究力量布局三个角度进行了分析。结果显示,大数据领域研究正处于中前期加速发展期,学科交叉性强;研究中心正在向中、美、...文章以1996-2015年Web of Science数据库收录的大数据领域文献为研究样本,从总体态势、空间格局演变特征和当前研究力量布局三个角度进行了分析。结果显示,大数据领域研究正处于中前期加速发展期,学科交叉性强;研究中心正在向中、美、英、德等多个核心区演变,呈现"核心-边缘"结构,并且各国研发实力相差悬殊,核心区主要发达国家研究力量和影响力稳步增长,而中国及亚洲地区的研究成果产出量增长较快,但研究成果学术质量亟待提升。展开更多
文摘In the data retrieval process of the Data recommendation system,the matching prediction and similarity identification take place a major role in the ontology.In that,there are several methods to improve the retrieving process with improved accuracy and to reduce the searching time.Since,in the data recommendation system,this type of data searching becomes complex to search for the best matching for given query data and fails in the accuracy of the query recommendation process.To improve the performance of data validation,this paper proposed a novel model of data similarity estimation and clustering method to retrieve the relevant data with the best matching in the big data processing.In this paper advanced model of the Logarithmic Directionality Texture Pattern(LDTP)method with a Metaheuristic Pattern Searching(MPS)system was used to estimate the similarity between the query data in the entire database.The overall work was implemented for the application of the data recommendation process.These are all indexed and grouped as a cluster to form a paged format of database structure which can reduce the computation time while at the searching period.Also,with the help of a neural network,the relevancies of feature attributes in the database are predicted,and the matching index was sorted to provide the recommended data for given query data.This was achieved by using the Distributional Recurrent Neural Network(DRNN).This is an enhanced model of Neural Network technology to find the relevancy based on the correlation factor of the feature set.The training process of the DRNN classifier was carried out by estimating the correlation factor of the attributes of the dataset.These are formed as clusters and paged with proper indexing based on the MPS parameter of similarity metric.The overall performance of the proposed work can be evaluated by varying the size of the training database by 60%,70%,and 80%.The parameters that are considered for performance analysis are Precision,Recall,F1-score and the accuracy of data retrieval,the query recommendation output,and comparison with other state-of-art methods.
文摘针对单个传感器采集信息不准确的问题,提出一种基于Bayes估计的受限空间探测数据融合算法.首先,通过分析探测信号的组成结构,利用滤波、限幅、阶跃信号去除等方法,解决了信号干扰问题,提高了特征参量的显著性;其次,结合数据融合架构的动态性特征,给出合理假设,组合先验网络与转移网络,共同建立动态Bayes网络模型,得到融合目标函数;最后,通过引入正态分布研究探测值的不确定性,将探测节点视为似然函数,推导融合后的最大后验概率,以融合加权平均误差比为指标,通过“两两相遇”的方式实现多类型探测数据融合.仿真实验结果表明,该算法解决了信号冗余问题,数据融合效果较好,火灾整体漏报次数较少,数据融合时间最高值仅为2.4 s.
文摘文章以1996-2015年Web of Science数据库收录的大数据领域文献为研究样本,从总体态势、空间格局演变特征和当前研究力量布局三个角度进行了分析。结果显示,大数据领域研究正处于中前期加速发展期,学科交叉性强;研究中心正在向中、美、英、德等多个核心区演变,呈现"核心-边缘"结构,并且各国研发实力相差悬殊,核心区主要发达国家研究力量和影响力稳步增长,而中国及亚洲地区的研究成果产出量增长较快,但研究成果学术质量亟待提升。
