The aim of this paper is to present a distributed algorithm for big data classification, and its application for Magnetic Resonance Images (MRI) segmentation. We choose the well-known classification method which is th...The aim of this paper is to present a distributed algorithm for big data classification, and its application for Magnetic Resonance Images (MRI) segmentation. We choose the well-known classification method which is the c-means method. The proposed method is introduced in order to perform a cognitive program which is assigned to be implemented on a parallel and distributed machine based on mobile agents. The main idea of the proposed algorithm is to execute the c-means classification procedure by the Mobile Classification Agents (Team Workers) on different nodes on their data at the same time and provide the results to their Mobile Host Agent (Team Leader) which computes the global results and orchestrates the classification until the convergence condition is achieved and the output segmented images will be provided from the Mobile Classification Agents. The data in our case are the big data MRI image of size (m × n) which is splitted into (m × n) elementary images one per mobile classification agent to perform the classification procedure. The experimental results show that the use of the distributed architecture improves significantly the big data segmentation efficiency.展开更多
In the era of big data,the ways people work,live and think have changed dramatically,and the social governance system is also being restructured.Achieving intelligent social governance has now become a national strate...In the era of big data,the ways people work,live and think have changed dramatically,and the social governance system is also being restructured.Achieving intelligent social governance has now become a national strategy.The application of big data technology to counterterrorism efforts has become a powerful weapon for all countries.However,due to the uncertainty,difficulty of interpretation and potential risk of discrimination in big data technology and algorithm models,basic human rights,freedom and even ethics are likely to be impacted and challenged.As a result,there is an urgent need to prioritize basic human rights and regulate the application of big data for counter terrorism purposes.The legislation and law enforcement regarding the use of big data to counter terrorism must be subject to constitutional and other legal reviews,so as to strike a balance between safeguarding national security and protecting basic human rights.展开更多
超大量的数据从诸如传感器、社交媒体、互联网应用等物联网产生,这些数据被统称为大数据。传统的工具和技术无法处理大数据。为了从大量数据中提取出对新技术有益的信息,大数据的挖掘尤为重要。非常受关注的关联规则挖掘和高频数据项挖...超大量的数据从诸如传感器、社交媒体、互联网应用等物联网产生,这些数据被统称为大数据。传统的工具和技术无法处理大数据。为了从大量数据中提取出对新技术有益的信息,大数据的挖掘尤为重要。非常受关注的关联规则挖掘和高频数据项挖掘都需要在内存中调入全部数据,但是大量的数据并不适合内存存储。为解决这一难题,业界提出了MapReduce进行并行的大数据处理。本文提出的改进Big FIM算法(Improved Big FIM,IBFIM)运行于MapReduce架构下用于大数据的挖掘.IBFIM相对于Big FIM增加了对更大规模数据的支持并提高了数据挖掘的速度。该研究为更快速、更高效地并行挖掘大数据内容提供参考。展开更多
文摘The aim of this paper is to present a distributed algorithm for big data classification, and its application for Magnetic Resonance Images (MRI) segmentation. We choose the well-known classification method which is the c-means method. The proposed method is introduced in order to perform a cognitive program which is assigned to be implemented on a parallel and distributed machine based on mobile agents. The main idea of the proposed algorithm is to execute the c-means classification procedure by the Mobile Classification Agents (Team Workers) on different nodes on their data at the same time and provide the results to their Mobile Host Agent (Team Leader) which computes the global results and orchestrates the classification until the convergence condition is achieved and the output segmented images will be provided from the Mobile Classification Agents. The data in our case are the big data MRI image of size (m × n) which is splitted into (m × n) elementary images one per mobile classification agent to perform the classification procedure. The experimental results show that the use of the distributed architecture improves significantly the big data segmentation efficiency.
文摘In the era of big data,the ways people work,live and think have changed dramatically,and the social governance system is also being restructured.Achieving intelligent social governance has now become a national strategy.The application of big data technology to counterterrorism efforts has become a powerful weapon for all countries.However,due to the uncertainty,difficulty of interpretation and potential risk of discrimination in big data technology and algorithm models,basic human rights,freedom and even ethics are likely to be impacted and challenged.As a result,there is an urgent need to prioritize basic human rights and regulate the application of big data for counter terrorism purposes.The legislation and law enforcement regarding the use of big data to counter terrorism must be subject to constitutional and other legal reviews,so as to strike a balance between safeguarding national security and protecting basic human rights.
文摘超大量的数据从诸如传感器、社交媒体、互联网应用等物联网产生,这些数据被统称为大数据。传统的工具和技术无法处理大数据。为了从大量数据中提取出对新技术有益的信息,大数据的挖掘尤为重要。非常受关注的关联规则挖掘和高频数据项挖掘都需要在内存中调入全部数据,但是大量的数据并不适合内存存储。为解决这一难题,业界提出了MapReduce进行并行的大数据处理。本文提出的改进Big FIM算法(Improved Big FIM,IBFIM)运行于MapReduce架构下用于大数据的挖掘.IBFIM相对于Big FIM增加了对更大规模数据的支持并提高了数据挖掘的速度。该研究为更快速、更高效地并行挖掘大数据内容提供参考。
