Big data clustering plays an important role in the field of data processing in wireless sensor networks.However,there are some problems such as poor clustering effect and low Jaccard coefficient.This paper proposes a ...Big data clustering plays an important role in the field of data processing in wireless sensor networks.However,there are some problems such as poor clustering effect and low Jaccard coefficient.This paper proposes a novel big data clustering optimization method based on intuitionistic fuzzy set distance and particle swarm optimization for wireless sensor networks.This method combines principal component analysis method and information entropy dimensionality reduction to process big data and reduce the time required for data clustering.A new distance measurement method of intuitionistic fuzzy sets is defined,which not only considers membership and non-membership information,but also considers the allocation of hesitancy to membership and non-membership,thereby indirectly introducing hesitancy into intuitionistic fuzzy set distance.The intuitionistic fuzzy kernel clustering algorithm is used to cluster big data,and particle swarm optimization is introduced to optimize the intuitionistic fuzzy kernel clustering method.The optimized algorithm is used to obtain the optimization results of wireless sensor network big data clustering,and the big data clustering is realized.Simulation results show that the proposed method has good clustering effect by comparing with other state-of-the-art clustering methods.展开更多
Recently,internet stimulates the explosive progress of knowledge discovery in big volume data resource,to dig the valuable and hidden rules by computing.Simultaneously,the wireless channel measurement data reveals big...Recently,internet stimulates the explosive progress of knowledge discovery in big volume data resource,to dig the valuable and hidden rules by computing.Simultaneously,the wireless channel measurement data reveals big volume feature,considering the massive antennas,huge bandwidth and versatile application scenarios.This article firstly presents a comprehensive survey of channel measurement and modeling research for mobile communication,especially for 5th Generation(5G) and beyond.Considering the big data research progress,then a cluster-nuclei based model is proposed,which takes advantages of both the stochastical model and deterministic model.The novel model has low complexity with the limited number of cluster-nuclei while the cluster-nuclei has the physical mapping to real propagation objects.Combining the channel properties variation principles with antenna size,frequency,mobility and scenario dug from the channel data,the proposed model can be expanded in versatile application to support future mobile research.展开更多
There are a lot of biological and experimental data from genomics, proteomics, drug screening, medicinal chemistry, etc. A large amount of data must be analyzed by special methods of statistics, bioinformatics, and co...There are a lot of biological and experimental data from genomics, proteomics, drug screening, medicinal chemistry, etc. A large amount of data must be analyzed by special methods of statistics, bioinformatics, and computer science. Big data analysis is an effective way to build scientific hypothesis and explore internal mechanism.Here, gene expression is taken as an example to illustrate the basic procedure of the big data analysis.展开更多
1 Introduction PCDs are generated in continental arcs in response to plate converging processes(subduction and collision)(Hou et al.,2009;Richards,2013).It is generally accepted that the formation of PCDs is associate...1 Introduction PCDs are generated in continental arcs in response to plate converging processes(subduction and collision)(Hou et al.,2009;Richards,2013).It is generally accepted that the formation of PCDs is associated with igneous activities either originating from lower crust or upper mantle,with contributions of crusts during the evolution of continental lithosphere.展开更多
Cloud computing allows scalability at a lower cost for data analytics in a big data environment. This paradigm considers the dimensioning of resources to process different volumes of data, minimizing the response time...Cloud computing allows scalability at a lower cost for data analytics in a big data environment. This paradigm considers the dimensioning of resources to process different volumes of data, minimizing the response time of big data. This work proposes a performance and availability evaluation of big data environments in the private cloud through a methodology and stochastic and combinatorial models considering performance metrics such as execution times, processor utilization, memory utilization, and availability. The proposed methodology considers objective activities, performance, and availability modeling to evaluate the private cloud environment. A performance model based on stochastic Petrinets is adopted to evaluate the big data environment on the private cloud. Reliability block diagram models are adopted to evaluate the availability of big environment data in the private cloud. Two case studies based on the CloudStack platform and Hadoop cluster are adopted to demonstrate the viability of the proposed methodologies and models. Case Study 1 evaluated the performance metrics of the Hadoop cluster in the private cloud, considering different service offerings, workloads, and the number of data sets. The sentiment analysis technique is used in tweets from users with symptoms of depression to generate the analyzed datasets. Case Study 2 evaluated the availability of big data environments in the private cloud.展开更多
Big data is a highlighted challenge for many fields with the rapid expansion of large-volume, complex, and fast-growing sources of data. Mining from big data is required for exploring the essence of data and providing...Big data is a highlighted challenge for many fields with the rapid expansion of large-volume, complex, and fast-growing sources of data. Mining from big data is required for exploring the essence of data and providing meaningful information. To this end, we have previously introduced the theory of physical field to explore relations between objects in data space and proposed a framework of data field to discover the underlying distribution of big data. This paper concerns an overview of big data mining by the use of data field. It mainly discusses the theory of data field and different aspects of applications including feature selection for high-dimensional data, clustering, and the recognition of facial expression in human-computer interaction. In these applications, data field is employed to capture the intrinsic distribution of data objects for selecting meaningful features, fast clustering, and describing variation of facial expression. It is expected that our contributions would help overcome the problems in accordance with big data.展开更多
基金2021 Scientific Research Funding Project of Liaoning Provincial Education Department(Research and implementation of university scientific research information platform serving the transformation of achievements).
文摘Big data clustering plays an important role in the field of data processing in wireless sensor networks.However,there are some problems such as poor clustering effect and low Jaccard coefficient.This paper proposes a novel big data clustering optimization method based on intuitionistic fuzzy set distance and particle swarm optimization for wireless sensor networks.This method combines principal component analysis method and information entropy dimensionality reduction to process big data and reduce the time required for data clustering.A new distance measurement method of intuitionistic fuzzy sets is defined,which not only considers membership and non-membership information,but also considers the allocation of hesitancy to membership and non-membership,thereby indirectly introducing hesitancy into intuitionistic fuzzy set distance.The intuitionistic fuzzy kernel clustering algorithm is used to cluster big data,and particle swarm optimization is introduced to optimize the intuitionistic fuzzy kernel clustering method.The optimized algorithm is used to obtain the optimization results of wireless sensor network big data clustering,and the big data clustering is realized.Simulation results show that the proposed method has good clustering effect by comparing with other state-of-the-art clustering methods.
基金supported in part by National Natural Science Foundation of China (61322110, 6141101115)Doctoral Fund of Ministry of Education (201300051100013)
文摘Recently,internet stimulates the explosive progress of knowledge discovery in big volume data resource,to dig the valuable and hidden rules by computing.Simultaneously,the wireless channel measurement data reveals big volume feature,considering the massive antennas,huge bandwidth and versatile application scenarios.This article firstly presents a comprehensive survey of channel measurement and modeling research for mobile communication,especially for 5th Generation(5G) and beyond.Considering the big data research progress,then a cluster-nuclei based model is proposed,which takes advantages of both the stochastical model and deterministic model.The novel model has low complexity with the limited number of cluster-nuclei while the cluster-nuclei has the physical mapping to real propagation objects.Combining the channel properties variation principles with antenna size,frequency,mobility and scenario dug from the channel data,the proposed model can be expanded in versatile application to support future mobile research.
文摘There are a lot of biological and experimental data from genomics, proteomics, drug screening, medicinal chemistry, etc. A large amount of data must be analyzed by special methods of statistics, bioinformatics, and computer science. Big data analysis is an effective way to build scientific hypothesis and explore internal mechanism.Here, gene expression is taken as an example to illustrate the basic procedure of the big data analysis.
基金supported by the National Key R&D Program of China(Grant No.2016YFC0600501)the National Natural Science Foundation of China(NSFC)(Grant No.41430320).
文摘1 Introduction PCDs are generated in continental arcs in response to plate converging processes(subduction and collision)(Hou et al.,2009;Richards,2013).It is generally accepted that the formation of PCDs is associated with igneous activities either originating from lower crust or upper mantle,with contributions of crusts during the evolution of continental lithosphere.
文摘Cloud computing allows scalability at a lower cost for data analytics in a big data environment. This paradigm considers the dimensioning of resources to process different volumes of data, minimizing the response time of big data. This work proposes a performance and availability evaluation of big data environments in the private cloud through a methodology and stochastic and combinatorial models considering performance metrics such as execution times, processor utilization, memory utilization, and availability. The proposed methodology considers objective activities, performance, and availability modeling to evaluate the private cloud environment. A performance model based on stochastic Petrinets is adopted to evaluate the big data environment on the private cloud. Reliability block diagram models are adopted to evaluate the availability of big environment data in the private cloud. Two case studies based on the CloudStack platform and Hadoop cluster are adopted to demonstrate the viability of the proposed methodologies and models. Case Study 1 evaluated the performance metrics of the Hadoop cluster in the private cloud, considering different service offerings, workloads, and the number of data sets. The sentiment analysis technique is used in tweets from users with symptoms of depression to generate the analyzed datasets. Case Study 2 evaluated the availability of big data environments in the private cloud.
文摘Big data is a highlighted challenge for many fields with the rapid expansion of large-volume, complex, and fast-growing sources of data. Mining from big data is required for exploring the essence of data and providing meaningful information. To this end, we have previously introduced the theory of physical field to explore relations between objects in data space and proposed a framework of data field to discover the underlying distribution of big data. This paper concerns an overview of big data mining by the use of data field. It mainly discusses the theory of data field and different aspects of applications including feature selection for high-dimensional data, clustering, and the recognition of facial expression in human-computer interaction. In these applications, data field is employed to capture the intrinsic distribution of data objects for selecting meaningful features, fast clustering, and describing variation of facial expression. It is expected that our contributions would help overcome the problems in accordance with big data.
