The Impact of Online Networks and Big Data in Life Sciences

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.

Content Centric Networking: A New Approach to Big Data Distribution

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.

Internet of Vehicles in Big Data Era

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.

The Roles of 5G Mobile Broadband in the Development of IoT, Big Data, Cloud and SDN

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.

Decision Model of Knowledge Transfer in Big Data Environment

A decision model of knowledge transfer is presented on the basis of the characteristics of knowledge transfer in a big data environment.This model can determine the weight of knowledge transferred from another enterprise or from a big data provider.Numerous simulation experiments are implemented to test the efficiency of the optimization model.Simulation experiment results show that when increasing the weight of knowledge from big data knowledge provider,the total discount expectation of profits will increase,and the transfer cost will be reduced.The calculated results are in accordance with the actual economic situation.The optimization model can provide useful decision support for enterprises in a big data environment.

Big Data for Precision Medicine

This article focuses on the potential impact of big data analysis to improve health, prevent and detect disease at an earlier stage, and personalize interventions. The role that big data analytics may have in interrogating the patient electronic health record toward improved clinical decision support is discussed. Weexamine developments in pharmacogenetics that have increased our appreciation of the reasons why patients respond differently to chemotherapy. We also assess the expansion of online health communications and the way in which this data may be capitalized on in order to detect public health threats and control or contain epidemics. Finally, we describe how a new generation of wearable and implantable body sensors may improve wellbeing, streamline management of chronic diseases, and improve the quality of surgical implants.

E-Healthcare Supported by Big Data

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.

Exploring the Big Data Using a Rigorous and Quantitative Causality Analysis

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.

Artificial Intelligence Self-Organising (AI-SON) Frameworks for 5G-Enabled Networks: A Review

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.

基于邻域粗集神经网络的大数据特征分类系统

为提升主机元件对大数据的分类准确性,尽可能地避免数据误传,提出基于邻域粗集神经网络的大数据特征分类系统。在邻域粗集神经网络中,完成对邻域系数的粒化处理,通过逼近运算的方式,使神经网络模型快速趋于稳定。选取大数据特征调制信息,借助调制识别器元件控制大数据特征的导出方向,结合关联信道组织完成数据特征的多标合并处理。实验表明,利用该系统可将大数据的单位召回率提升至65%,能够促进主机元件对大数据的准确分类。

大数据与计算模型

当前,人工智能持续升温,大语言模型吸引了众多人士的关注,并在全球范围内掀起了一股热潮。人工智能的成功本质上不是大算力“出奇迹”,而是改变了计算模型。首先,肯定了数据对于人工智能的基础性作用,指出合成数据将是未来数据的主要来源。然后,回顾了计算模型的发展历程,重点介绍了神经网络模型与图灵模型的历史性竞争;指出了大模型的重要标志是机器涌现智能,强调大模型的本质是“压缩”;分析了大模型产生“幻觉”的原因。最后,呼吁科技界在智能化科研中要重视大科学模型。

基于微服务和物联网的配电网智能计量架构

为实现电力系统向智能电网模式的演进,依赖于配电网(DN)的自动化,需要新的基础设施、技术和应用。提出一种基于微服务和物联网(IoT)的配电网自动化的智能计量设施,支持各种有助于实现配电网自动化和管理的服务。首先,在测量基础设施中对特定硬件、设备和底层协议进行抽线;然后,利用消息队列遥测传输(MQTT)作为以事件为导向的发布/订阅协议来实现,同时支持发布/订阅和请求/响应机制来实现;最后,使用以大数据为导向的可群集软件模块来实现可扩展性。分布式状态估计(SE)和网络拓扑重构算法验证了智能计量设施支持交互和协同操作。

计算机网络安全在大数据系统中的应用与研究

随着信息技术的快速发展,大数据系统在各行各业逐渐普及。然而,伴随着大规模数据的处理和传输,计算机网络安全问题逐渐显现。基于此,本文探讨了大数据系统中的网络安全挑战,分析计算机网络安全在大数据系统中的应用,提出大数据系统网络安全的未来发展方向。

通信网络的监控与优化平台在新媒体下的应用研究

在新媒体时代背景下,通信网络监控与优化平台面临着新的挑战与机遇。文章研究了在新媒体环境下,通信网络监控与优化平台的应用与发展。首先,分析新媒体给通信网络带来的流量变化、用户需求多样性以及实时性要求等特点,进而探讨监控与优化平台如何适应这些变化。其次,研究监控与优化平台在新媒体下的关键技术,包括大数据分析、人工智能算法等。这些技术为平台的智能化、自动化和高效化提供了有力支持。最后,通过实际案例分析监控与优化平台在新媒体领域的应用效果,展示其在提升网络性能、优化用户体验以及保障信息安全等方面的积极作用。文章研究对于推动通信网络监控与优化平台在新媒体时代的发展具有重要的参考价值。

配电网监测数据索引集群的分布缓存方法

针对配电网监测数据急剧增加导致配电网监控系统响应时间过长的问题,本文提出一种热点数据部分链表分布缓存方法。首先,引入缓存技术并分析原有热点数据链表缓存方法的局限性,提出一种新的缓存方法以提高缓存管理效率;然后,设计预热查询实现索引系统的热启动从而提高索引系统的缓存命中率;最后,以配电网监测数据为算例,验证所提方法的效果。测试结果表明,所提出的缓存方法及设计的预热查询能在不同查询条件下大幅减少索引集群的查询时间,有效提高配电网监测数据索引集群的查询效率。

基于卷积神经网络的大数据图像智能识别方法研究

利用卷积神经网络进行图像数据的识别与处理,能有效提高大数据图像识别的智能性,可以广泛应用于文学艺术、商务业务、工程建设等多个领域。实验表明,基于卷积神经网络的大数据图像智能识别方法误识率较低,具有明显的应用优势。

跨省域智慧能源管理大数据平台架构及关键技术研究与实践

随着广东省能源集团公司下属电站的不断建设,集团公司对各个电站的运营、运维、人员的管理问题愈发凸显。为有效提升各能源场站的综合管理水平,通过搭建基于物联网的智慧能源管理大数据平台对能源场进行集约管理。系统以工业互联网为硬件基础,以大数据平台为软件核心,实现了系统对风电场、光伏电站、煤层气电站等区域的实时远程监控及各项业务管理。实践证明,本系统能减少运行维护成本,提升集团公司的整体核心竞争力。