Advances in Information Technology (IT) have enhanced our ability to gather, collect and analyze information from individuals and specific groups of people online. The emergence of online networks has facilitated conn...Advances in Information Technology (IT) have enhanced our ability to gather, collect and analyze information from individuals and specific groups of people online. The emergence of online networks has facilitated connections between individuals by leveraging data exchange in a variety of fields. Online networking in life sciences transforms data collection into actionable information that will improve individual and population health, deliver effective therapies and, consequently, reduce the cost of healthcare. These novel tools might also have a direct impact in personalized medicine programs, since the adoption of new products by health care professionals in life sciences and peer-to-peer learning could be improved using social networks and big data analytics. However, one of the main concerns of information exchange online is data privacy. In this article, we will review how online networks and big data analytics are impacting the life sciences sector.展开更多
In this paper, we explore network architecture anal key technologies for content-centric networking （CCN）, an emerging networking technology in the big-data era. We descrihe the structure anti operation mechanism of...In this paper, we explore network architecture anal key technologies for content-centric networking （CCN）, an emerging networking technology in the big-data era. We descrihe the structure anti operation mechanism of tl CCN node. Then we discuss mobility management, routing strategy, and caching policy in CCN. For better network performance, we propose a probability cache replacement policy that is based on cotent popularity. We also propose and evaluate a probability cache with evicted copy-up decision policy.展开更多
As the rapid development of automotive telematics,modern vehicles are expected to be connected through heterogeneous radio access technologies and are able to exchange massive information with their surrounding enviro...As the rapid development of automotive telematics,modern vehicles are expected to be connected through heterogeneous radio access technologies and are able to exchange massive information with their surrounding environment. By significantly expanding the network scale and conducting both real-time and long-term information processing, the traditional Vehicular AdHoc Networks(VANETs) are evolving to the Internet of Vehicles(Io V), which promises efficient and intelligent prospect for the future transportation system. On the other hand, vehicles are not only consuming but also generating a huge amount and enormous types of data, which is referred to as Big Data. In this article, we first investigate the relationship between Io V and big data in vehicular environment, mainly on how Io V supports the transmission, storage, computing of the big data, and how Io V benefits from big data in terms of Io V characterization,performance evaluation and big data assisted communication protocol design. We then investigate the application of Io V big data in autonomous vehicles. Finally, the emerging issues of the big data enabled Io V are discussed.展开更多
The fast technology development of 5G mobile broadband (5G), Internet of Things (IoT), Big Data Analytics (Big Data), Cloud Computing (Cloud) and Software Defined Networks (SDN) has made those technologies one after a...The fast technology development of 5G mobile broadband (5G), Internet of Things (IoT), Big Data Analytics (Big Data), Cloud Computing (Cloud) and Software Defined Networks (SDN) has made those technologies one after another and created strong interdependence among one another. For example, IoT applications that generate small data with large volume and fast velocity will need 5G with characteristics of high data rate and low latency to transmit such data faster and cheaper. On the other hand, those data also need Cloud to process and to store and furthermore, SDN to provide scalable network infrastructure to transport this large volume of data in an optimal way. This article explores the technical relationships among the development of IoT, Big Data, Cloud, and SDN in the coming 5G era and illustrates several ongoing programs and applications at National Chiao Tung University that are based on the converging of those technologies.展开更多
The era of open information in healthcare has arrived. E-healthcare supported by big data supports the move toward greater trans-parency in healthcare by making decades of stored health data searchable and usable. Thi...The era of open information in healthcare has arrived. E-healthcare supported by big data supports the move toward greater trans-parency in healthcare by making decades of stored health data searchable and usable. This paper gives an overview the e-health-care architecture. We discuss the four layers of the architecture-data collection, data transport, data storage, and data analysis-as well as the challenges of data security, data privacy, real-time delivery, and open standard interface. We discuss the necessity of establishing an impeccably secure access mechanism and of enacting strong laws to protect patient privacy.展开更多
Causal analysis is a powerful tool to unravel the data complexity and hence provide clues to achieving, say, better platform design, efficient interoperability and service management, etc. Data science will surely ben...Causal analysis is a powerful tool to unravel the data complexity and hence provide clues to achieving, say, better platform design, efficient interoperability and service management, etc. Data science will surely benefit from the advancement in this field. Here we introduce into this community a recent finding in physics on causality and the subsequent rigorous and quantitative causality analysis. The resulting formula is concise in form, involving only the common statistics namely sample covariance. A corollary is that causation implies correlation, but not vice versa, resolving the long-standing philosophical debate over correlation versus causation. The applicability to big data analysis is validated with time series purportedly generated with hidden processes. As a demonstration, a preliminary application to the gross domestic product (GDP) data of United States, China, and Japan reveals some subtle USA-China-Japan relations in certain periods. 展开更多
Increasing complexity of industrial products and manufacturing processes have challenged conventional statistics based quality management approaches in the cir- cumstances of dynamic production. A Bayesian network and...Increasing complexity of industrial products and manufacturing processes have challenged conventional statistics based quality management approaches in the cir- cumstances of dynamic production. A Bayesian network and big data analytics integrated approach for manufacturing process quality analysis and control is proposed. Based on Hadoop distributed architecture and MapReduce parallel computing model, big volume and variety quality related data generated during the manufacturing process could be dealt with. Artificial intelligent algorithms, including Bayesian network learning, classification and reasoning, are embedded into the Reduce process. Relying on the ability of the Bayesian network in dealing with dynamic and uncertain problem and the parallel computing power of MapReduce, Bayesian net- work of impact factors on quality are built based on prior probability distribution and modified with posterior probability distribution. A case study on hull segment manufacturing precision management for ship and offshore platform building shows that computing speed accelerates almost directly pro- portionally to the increase of computing nodes. It is also proved that the proposed model is feasible for locating and reasoning of root causes, forecasting of manufacturing outcome, and intelligent decision for precision problem solving. The inte- gration ofbigdata analytics and BN method offers a whole new perspective in manufacturing quality control.展开更多
New sensing and wireless technologies generate massive data. This paper proposes an efficient Bayesian network to evaluate the slope safety using large-quantity field monitoring information with underlying physical me...New sensing and wireless technologies generate massive data. This paper proposes an efficient Bayesian network to evaluate the slope safety using large-quantity field monitoring information with underlying physical mechanisms. A Bayesian network for a slope involving correlated material properties and dozens of observational points is constructed.展开更多
Mobile operators face the challenge of how to best design a service-centric network that can effectively process the rapidly increasing number of bandwidth-intensive user requests while providing a higher quality of e...Mobile operators face the challenge of how to best design a service-centric network that can effectively process the rapidly increasing number of bandwidth-intensive user requests while providing a higher quality of experience(QoE). Existing content distribution networks(CDN) and mobile content distribution networks(mCDN) have both latency and throughput limitations due to being multiple network hops away from end-users. Here, we first propose a new Personalized Edge Caching System(PECS) architecture that employs big data analytics and mobile edge caching to provide personalized service access at the edge of the mobile network. Based on the proposed system architecture, the edge caching strategy based on user behavior and trajectory is analyzed. Employing our proposed PECS strategies, we use data mining algorithms to analyze the personalized trajectory and service usage patterns. Our findings provide guidance on how key technologies of PECS can be employed for current and future networks. Finally, we highlight the challenges associated with realizing such a system in 5G and beyond.展开更多
With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and ...With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and poor accuracy, when handling big data. In this study, the research object was the asynchronous motor in the drivetrain diagnostics simulator system. The vibration signals of different fault motors were collected. The raw signal was pretreated using short time Fourier transform （STFT） to obtain the corresponding time-frequency map. Then, the feature of the time-frequency map was adap- tively extracted by using a convolutional neural network （CNN）. The effects of the pretreatment method, and the hyper parameters of network diagnostic accuracy, were investigated experimentally. The experimental results showed that the influence of the preprocessing method is small, and that the batch-size is the main factor affecting accuracy and training efficiency. By investigating feature visualization, it was shown that, in the case of big data, the extracted CNN features can represent complex mapping relationships between signal and health status, and can also overcome the prior knowledge and engineering experience requirement for feature extraction, which is used by tra- ditional diagnosis methods. This paper proposes a new method, based on STFT and CNN, which can complete motor fault diagnosis tasks more intelligently and accurately.展开更多
The fifth generation (5G) networks will support the rapid emergence of Internet of Things (IoT) devices operating in a heterogeneous network (HetNet) system. These 5G-enabled IoT devices will result in a surge in data...The fifth generation (5G) networks will support the rapid emergence of Internet of Things (IoT) devices operating in a heterogeneous network (HetNet) system. These 5G-enabled IoT devices will result in a surge in data traffic for Mobile Network Operators (MNOs) to handle. At the same time, MNOs are preparing for a paradigm shift to decouple the control and forwarding plane in a Software-Defined Networking (SDN) architecture. Artificial Intelligence powered Self-Organising Networks (AI-SON) can fit into the SDN architecture by providing prediction and recommender systems to minimise costs in supporting the MNO’s infrastructure. This paper presents a review report on AI-SON frameworks in 5G and SDN. The review considers the dynamic deployment and functions of the AI-SON frameworks, especially for SDN support and applications. Each module in the frameworks was discussed to ascertain its relevance based on the context of AI-SON and SDN integration. After examining each framework, the identified gaps are summarised as open issues for future works.展开更多
Recent emergence of diverse services have led to explosive traffic growth in cellular data networks. Understanding the service dynamics in large cellular networks is important for network design, trouble shooting, qua...Recent emergence of diverse services have led to explosive traffic growth in cellular data networks. Understanding the service dynamics in large cellular networks is important for network design, trouble shooting, quality of service(Qo E) support, and resource allocation. In this paper, we present our study to reveal the distributions and temporal patterns of different services in cellular data network from two different perspectives, namely service request times and service duration. Our study is based on big traffic data, which is parsed to readable records by our Hadoop-based packet parsing platform, captured over a week-long period from a tier-1 mobile operator's network in China. We propose a Zipf's ranked model to characterize the distributions of traffic volume, packet, request times and duration of cellular services. Two-stage method(Self-Organizing Map combined with kmeans) is first used to cluster time series of service into four request patterns and three duration patterns. These seven patterns are combined together to better understand the fine-grained temporal patterns of service in cellular network. Results of our distribution models and temporal patterns present cellular network operators with a better understanding of the request and duration characteristics of service, which of great importance in network design, service generation and resource allocation.展开更多
以旅游大数据为基础,考虑长时间范围内的滞后效应以及不同搜索强度指数(Search Intensity Index,SII)之间的多任务影响,提出一种基于大数据的多任务旅游信息分析(Multi-tasking Tourism Information Analysis Based on Big Data,MTIABD...以旅游大数据为基础,考虑长时间范围内的滞后效应以及不同搜索强度指数(Search Intensity Index,SII)之间的多任务影响,提出一种基于大数据的多任务旅游信息分析(Multi-tasking Tourism Information Analysis Based on Big Data,MTIABD)框架。使用融合信息重排序技术预测旅游需求,具体根据图引导结构模拟历史变量对未来变量的滞后影响。每个变量通过时间维度上的卷积神经网络(Convolutional Neural Network,CNN)进行独立编码,利用二分图动态建模滞后效应,通过图聚合进行挖掘,实现对旅游需求的精准预测。基于上述技术,构建旅游需求预测系统,旅游者能够根据需求检索不同景点的信息。在真实数据集上进行大量实验,结果表明所提出的MTIABD框架在一步和多步预测方面均优于现有方法。在平均绝对百分比误差(Mean Absolute Percentage Error,MAPE)指标下,相较于基于实例的多变量时间序列图预测框架(Instance-wise Graph-rased Framework for Multivariate Time Series Forecasting,IGMTF),MTIABD在HK-2021数据集上的性能提高了16.75%,在MO-2021数据集上的性能提高了19.79%。展开更多
文摘Advances in Information Technology (IT) have enhanced our ability to gather, collect and analyze information from individuals and specific groups of people online. The emergence of online networks has facilitated connections between individuals by leveraging data exchange in a variety of fields. Online networking in life sciences transforms data collection into actionable information that will improve individual and population health, deliver effective therapies and, consequently, reduce the cost of healthcare. These novel tools might also have a direct impact in personalized medicine programs, since the adoption of new products by health care professionals in life sciences and peer-to-peer learning could be improved using social networks and big data analytics. However, one of the main concerns of information exchange online is data privacy. In this article, we will review how online networks and big data analytics are impacting the life sciences sector.
基金supported by National Natural Science Foundation of China under Grant No.60872018 and No. 60902015Major National Science and Technology Project No. 2011ZX03005-004-03
文摘In this paper, we explore network architecture anal key technologies for content-centric networking （CCN）, an emerging networking technology in the big-data era. We descrihe the structure anti operation mechanism of tl CCN node. Then we discuss mobility management, routing strategy, and caching policy in CCN. For better network performance, we propose a probability cache replacement policy that is based on cotent popularity. We also propose and evaluate a probability cache with evicted copy-up decision policy.
基金supported by the National Natural Science Foundation of China(91638204)Natural Sciences and Engineering Research Council(NSERC)
文摘As the rapid development of automotive telematics,modern vehicles are expected to be connected through heterogeneous radio access technologies and are able to exchange massive information with their surrounding environment. By significantly expanding the network scale and conducting both real-time and long-term information processing, the traditional Vehicular AdHoc Networks(VANETs) are evolving to the Internet of Vehicles(Io V), which promises efficient and intelligent prospect for the future transportation system. On the other hand, vehicles are not only consuming but also generating a huge amount and enormous types of data, which is referred to as Big Data. In this article, we first investigate the relationship between Io V and big data in vehicular environment, mainly on how Io V supports the transmission, storage, computing of the big data, and how Io V benefits from big data in terms of Io V characterization,performance evaluation and big data assisted communication protocol design. We then investigate the application of Io V big data in autonomous vehicles. Finally, the emerging issues of the big data enabled Io V are discussed.
文摘The fast technology development of 5G mobile broadband (5G), Internet of Things (IoT), Big Data Analytics (Big Data), Cloud Computing (Cloud) and Software Defined Networks (SDN) has made those technologies one after another and created strong interdependence among one another. For example, IoT applications that generate small data with large volume and fast velocity will need 5G with characteristics of high data rate and low latency to transmit such data faster and cheaper. On the other hand, those data also need Cloud to process and to store and furthermore, SDN to provide scalable network infrastructure to transport this large volume of data in an optimal way. This article explores the technical relationships among the development of IoT, Big Data, Cloud, and SDN in the coming 5G era and illustrates several ongoing programs and applications at National Chiao Tung University that are based on the converging of those technologies.
基金the Natural Science Foundation of Guangdong Province, China (No.9151009001000021)the Ministry of Education of Guangdong Province Special Fund Funded Projects through the Cooperative of China (No.2009B090300341)+2 种基金the National Natural Science Foundation of China (No.61262013)the Open Fund of Guangdong Province Key Laboratory of Precision Equipment and Manufacturing Technology (No.PEMT1303)the Higher Vocational Education Teaching Reform Project of Guangdong Province (No.20130301011) for their support in this research
文摘The era of open information in healthcare has arrived. E-healthcare supported by big data supports the move toward greater trans-parency in healthcare by making decades of stored health data searchable and usable. This paper gives an overview the e-health-care architecture. We discuss the four layers of the architecture-data collection, data transport, data storage, and data analysis-as well as the challenges of data security, data privacy, real-time delivery, and open standard interface. We discuss the necessity of establishing an impeccably secure access mechanism and of enacting strong laws to protect patient privacy.
文摘Causal analysis is a powerful tool to unravel the data complexity and hence provide clues to achieving, say, better platform design, efficient interoperability and service management, etc. Data science will surely benefit from the advancement in this field. Here we introduce into this community a recent finding in physics on causality and the subsequent rigorous and quantitative causality analysis. The resulting formula is concise in form, involving only the common statistics namely sample covariance. A corollary is that causation implies correlation, but not vice versa, resolving the long-standing philosophical debate over correlation versus causation. The applicability to big data analysis is validated with time series purportedly generated with hidden processes. As a demonstration, a preliminary application to the gross domestic product (GDP) data of United States, China, and Japan reveals some subtle USA-China-Japan relations in certain periods.
基金Supported by 2015 Special Funds for Intelligent Manufacturing of China MIIT(Grant No.2015-415)National Natural Science Foundation of China(Grant No.71632008)
文摘Increasing complexity of industrial products and manufacturing processes have challenged conventional statistics based quality management approaches in the cir- cumstances of dynamic production. A Bayesian network and big data analytics integrated approach for manufacturing process quality analysis and control is proposed. Based on Hadoop distributed architecture and MapReduce parallel computing model, big volume and variety quality related data generated during the manufacturing process could be dealt with. Artificial intelligent algorithms, including Bayesian network learning, classification and reasoning, are embedded into the Reduce process. Relying on the ability of the Bayesian network in dealing with dynamic and uncertain problem and the parallel computing power of MapReduce, Bayesian net- work of impact factors on quality are built based on prior probability distribution and modified with posterior probability distribution. A case study on hull segment manufacturing precision management for ship and offshore platform building shows that computing speed accelerates almost directly pro- portionally to the increase of computing nodes. It is also proved that the proposed model is feasible for locating and reasoning of root causes, forecasting of manufacturing outcome, and intelligent decision for precision problem solving. The inte- gration ofbigdata analytics and BN method offers a whole new perspective in manufacturing quality control.
基金supported by the Research Grants Council of the Hong Kong SAR Government(Grant Nos.16202716 and C6012-15G)
文摘New sensing and wireless technologies generate massive data. This paper proposes an efficient Bayesian network to evaluate the slope safety using large-quantity field monitoring information with underlying physical mechanisms. A Bayesian network for a slope involving correlated material properties and dozens of observational points is constructed.
基金supported in part by the Fundamental Research Funds for the Central Universities of China (No. 2018CUCTJ078, CUC18A002-2)
文摘Mobile operators face the challenge of how to best design a service-centric network that can effectively process the rapidly increasing number of bandwidth-intensive user requests while providing a higher quality of experience(QoE). Existing content distribution networks(CDN) and mobile content distribution networks(mCDN) have both latency and throughput limitations due to being multiple network hops away from end-users. Here, we first propose a new Personalized Edge Caching System(PECS) architecture that employs big data analytics and mobile edge caching to provide personalized service access at the edge of the mobile network. Based on the proposed system architecture, the edge caching strategy based on user behavior and trajectory is analyzed. Employing our proposed PECS strategies, we use data mining algorithms to analyze the personalized trajectory and service usage patterns. Our findings provide guidance on how key technologies of PECS can be employed for current and future networks. Finally, we highlight the challenges associated with realizing such a system in 5G and beyond.
基金Supported by National Natural Science Foundation of China(Grant No.51405241,51505234,51575283)
文摘With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and poor accuracy, when handling big data. In this study, the research object was the asynchronous motor in the drivetrain diagnostics simulator system. The vibration signals of different fault motors were collected. The raw signal was pretreated using short time Fourier transform （STFT） to obtain the corresponding time-frequency map. Then, the feature of the time-frequency map was adap- tively extracted by using a convolutional neural network （CNN）. The effects of the pretreatment method, and the hyper parameters of network diagnostic accuracy, were investigated experimentally. The experimental results showed that the influence of the preprocessing method is small, and that the batch-size is the main factor affecting accuracy and training efficiency. By investigating feature visualization, it was shown that, in the case of big data, the extracted CNN features can represent complex mapping relationships between signal and health status, and can also overcome the prior knowledge and engineering experience requirement for feature extraction, which is used by tra- ditional diagnosis methods. This paper proposes a new method, based on STFT and CNN, which can complete motor fault diagnosis tasks more intelligently and accurately.
文摘The fifth generation (5G) networks will support the rapid emergence of Internet of Things (IoT) devices operating in a heterogeneous network (HetNet) system. These 5G-enabled IoT devices will result in a surge in data traffic for Mobile Network Operators (MNOs) to handle. At the same time, MNOs are preparing for a paradigm shift to decouple the control and forwarding plane in a Software-Defined Networking (SDN) architecture. Artificial Intelligence powered Self-Organising Networks (AI-SON) can fit into the SDN architecture by providing prediction and recommender systems to minimise costs in supporting the MNO’s infrastructure. This paper presents a review report on AI-SON frameworks in 5G and SDN. The review considers the dynamic deployment and functions of the AI-SON frameworks, especially for SDN support and applications. Each module in the frameworks was discussed to ascertain its relevance based on the context of AI-SON and SDN integration. After examining each framework, the identified gaps are summarised as open issues for future works.
基金supported by the National Basic Research Program of China (973 Program: 2013CB329004)
文摘Recent emergence of diverse services have led to explosive traffic growth in cellular data networks. Understanding the service dynamics in large cellular networks is important for network design, trouble shooting, quality of service(Qo E) support, and resource allocation. In this paper, we present our study to reveal the distributions and temporal patterns of different services in cellular data network from two different perspectives, namely service request times and service duration. Our study is based on big traffic data, which is parsed to readable records by our Hadoop-based packet parsing platform, captured over a week-long period from a tier-1 mobile operator's network in China. We propose a Zipf's ranked model to characterize the distributions of traffic volume, packet, request times and duration of cellular services. Two-stage method(Self-Organizing Map combined with kmeans) is first used to cluster time series of service into four request patterns and three duration patterns. These seven patterns are combined together to better understand the fine-grained temporal patterns of service in cellular network. Results of our distribution models and temporal patterns present cellular network operators with a better understanding of the request and duration characteristics of service, which of great importance in network design, service generation and resource allocation.
文摘以旅游大数据为基础,考虑长时间范围内的滞后效应以及不同搜索强度指数(Search Intensity Index,SII)之间的多任务影响,提出一种基于大数据的多任务旅游信息分析(Multi-tasking Tourism Information Analysis Based on Big Data,MTIABD)框架。使用融合信息重排序技术预测旅游需求,具体根据图引导结构模拟历史变量对未来变量的滞后影响。每个变量通过时间维度上的卷积神经网络(Convolutional Neural Network,CNN)进行独立编码,利用二分图动态建模滞后效应,通过图聚合进行挖掘,实现对旅游需求的精准预测。基于上述技术,构建旅游需求预测系统,旅游者能够根据需求检索不同景点的信息。在真实数据集上进行大量实验,结果表明所提出的MTIABD框架在一步和多步预测方面均优于现有方法。在平均绝对百分比误差(Mean Absolute Percentage Error,MAPE)指标下,相较于基于实例的多变量时间序列图预测框架(Instance-wise Graph-rased Framework for Multivariate Time Series Forecasting,IGMTF),MTIABD在HK-2021数据集上的性能提高了16.75%,在MO-2021数据集上的性能提高了19.79%。
