The Interdisciplinary Research of Big Data and Wireless Channel: A Cluster-Nuclei Based Channel Model

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.

Big Data in Chinese Government Governance: Analysis of Decision-Making Model Innovation and Practice

The 19th National Congress of the Communist Party of China has put forward higher requirements for Chinese government governance. The government governance has developed to a higher stage. Meanwhile, it faces more challenges, like lack of top-level design and information sharing. To develop a government governance decision-making innovation model, we should make good use of big data to mine in the grassroots government data management network. Both the characteristics of the times and the experience of the practice have proven that big data can empower government governance and promote the construction of a service-oriented government.

Data Modeling and Data Analytics: A Survey from a Big Data Perspective

These last years we have been witnessing a tremendous growth in the volume and availability of data. This fact results primarily from the emergence of a multitude of sources (e.g. computers, mobile devices, sensors or social networks) that are continuously producing either structured, semi-structured or unstructured data. Database Management Systems and Data Warehouses are no longer the only technologies used to store and analyze datasets, namely due to the volume and complex structure of nowadays data that degrade their performance and scalability. Big Data is one of the recent challenges, since it implies new requirements in terms of data storage, processing and visualization. Despite that, analyzing properly Big Data can constitute great advantages because it allows discovering patterns and correlations in datasets. Users can use this processed information to gain deeper insights and to get business advantages. Thus, data modeling and data analytics are evolved in a way that we are able to process huge amounts of data without compromising performance and availability, but instead by “relaxing” the usual ACID properties. This paper provides a broad view and discussion of the current state of this subject with a particular focus on data modeling and data analytics, describing and clarifying the main differences between the three main approaches in what concerns these aspects, namely: operational databases, decision support databases and Big Data technologies.

A New Efficient Obstacle Avoidance Control Method for Cars Based on Big Data and Just-in-Time Modeling

This paper provides a new obstacle avoidance control method for cars based on big data and just-in-time modeling. Just-in-time modeling is a new kind of data-driven control technique in the age of big data and is used in various real systems. The main property of the proposed method is that a gain and a control time which are parameters in the control input to avoid an encountered obstacle are computed from a database which includes a lot of driving data in various situations. Especially, the important advantage of the method is small computation time, and hence it realizes real-time obstacle avoidance control for cars. From some numerical simulations, it is showed that the new control method can make the car avoid various obstacles efficiently in comparison with the previous method.

Foundation Study on Wireless Big Data: Concept, Mining, Learning and Practices

Facing the development of future 5 G, the emerging technologies such as Internet of things, big data, cloud computing, and artificial intelligence is enhancing an explosive growth in data traffic. Radical changes in communication theory and implement technologies, the wireless communications and wireless networks have entered a new era. Among them, wireless big data(WBD) has tremendous value, and artificial intelligence(AI) gives unthinkable possibilities. However, in the big data development and artificial intelligence application groups, the lack of a sound theoretical foundation and mathematical methods is regarded as a real challenge that needs to be solved. From the basic problem of wireless communication, the interrelationship of demand, environment and ability, this paper intends to investigate the concept and data model of WBD, the wireless data mining, the wireless knowledge and wireless knowledge learning(WKL), and typical practices examples, to facilitate and open up more opportunities of WBD research and developments. Such research is beneficial for creating new theoretical foundation and emerging technologies of future wireless communications.

Use of community mobile phone big location data to recognize unusual patterns close to a pipeline which may indicate unauthorized activities and possible risk of damage

Damage caused by people and organizations unconnected with the pipeline management is a major risk faced by pipelines,and its consequences can have a huge impact.However,the present measures to monitor this have major problems such as time delays,overlooking threats,and false alarms.To overcome the disadvantages of these methods,analysis of big location data from mobile phone systems was applied to prevent third-party damage to pipelines,and a third-party damage prevention system was developed for pipelines including encryption mobile phone data,data preprocessing,and extraction of characteristic patterns.By applying this to natural gas pipelines,a large amount of location data was collected for data feature recognition and model analysis.Third-party illegal construction and occupation activities were discovered in a timely manner.This is important for preventing third-party damage to pipelines.

Big Data for Organizations: A Review

Big data challenges current information technologies (IT landscape) while promising a more competitive and efficient contributions to business organizations. What big data can contribute to is what organizations have been wanted for a long time ago. This paper presents the nature of big data and how organizations can advance their systems with big data technologies. By improving the efficiency and effectiveness of organizations, people can benefit the can take advantages of a more convenient life contributed by Information Technology.

Geological Database for Plate Tectonic Reconstruction:A Conceptual Model

We all live on one planet and geology has no borders.Countries that reside on different continents share the same architecture beneath the surface;they were once neighbors with common foundations.Interoperable geological data are now freely available to everyone for the benefit of society,demonstrating that geoscience can address both global and regional problems.Whilst increasingly large datasets("Big Data")provide clear opportunities(e.g.,Spina,2018).

Exploration and Realization of Several Key Problems of Geological Big Data

With the rapid development of technology,geological big data is increasing explosively,and plays an increasingly important position in the national economy(Zhang and Zhou,2017;Zhou et al.,2018).Governments and agencies attach great importance to the open internet service of geological big data and information at home,and abroad(Yan et al.,2013;Guo et al.,2014).The basic norms of western countries’geological data information services are rich and varied products.

Towards the Development of Best Data Security for Big Data

Big data is becoming a well-known buzzword and in active use in many areas. Because of the velocity, variety, and volume of big data, security and privacy issues are magnified, which results in the traditional protection mechanisms for structured small scale data are inadequate for big data. Sensitivities around big data security and privacy are a hurdle that organizations need to overcome. In this paper, we review the current data security in big data and analysis its feasibilities and obstacles. Besides, we also introduced intelligent analytics to enhance security with the proposed security intelligence model. This research aims to summarize, organize and classify the information available in the literature to identify any gaps in current research and suggest areas for scholars and security researchers for further investigation.

HOW BIG DATA MAKES CONSTRUCTION PROJECT RISK INTACT

Construction project is not a standalone engineering maneuver.It is closely linked to the well-being of local communities in concern.The city renovation in Beijing down center for Olympic 2008 transformed many antique architecture and regional landscape.It gave a world-recognized achievement in China s modem development and manifested a major milestone in China's economic development.In the course of metro construction projects,there are substantial interwoven municipal structures influencing the success of the projects,which including,but the least,all underground cables and ducts,sewage system,the power consumption of construction works,traffic diversion,air pollution,expatriate business activities and social security.There are many US and UK project insurance companies moving into Asia Pacific.They are doing re-insurance business on major construction guarantee,such as machinery damage,project on-time,power consumption,claims from contractors and communities.Environmental information,such as water quality,indoor and outdoor air quality,people inflow and lift waiting time play deterministic roles in construction's fit-touse.Big Data is a contemporary buzzword since 2013,and the key competence is to provide real time response to heuristic syndrome in order to make short-term prediction.This paper attempts to develop a conceptual model in big data for construction

Advance Techniques in Medical Imaging under Big Data Analysis: Covid-19 Images

Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. Accurate segmentation is required for volume determination, 3D rendering, radiation therapy, and surgery planning. In medical images, segmentation has traditionally been done by human experts. Substantial computational and storage requirements become especially acute when object orientation and scale have to be considered. Therefore, automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use. Many methods have been proposed to detect and segment 2D shapes, most of which involve template matching. Advanced segmentation techniques called Snakes or active contours have been used, considering deformable models or templates. The main purpose of this work is to apply segmentation techniques for the definition of 3D organs (anatomical structures) when big data information has been stored and must be organized by the doctors for medical diagnosis. The processes would be implemented in the CT images from patients with COVID-19.

A Surfing Concurrence Transaction Model for Key-Value NoSQL Databases

As more and more application systems related to big data were developed, NoSQL (Not Only SQL) database systems are becoming more and more popular. In order to add transaction features for some NoSQL database systems, many scholars have tried different techniques. Unfortunately, there is a lack of research on Redis’s transaction in the existing literatures. This paper proposes a transaction model for key-value NoSQL databases including Redis to make possible allowing users to access data in the ACID (Atomicity, Consistency, Isolation and Durability) way, and this model is vividly called the surfing concurrence transaction model. The architecture, important features and implementation principle are described in detail. The key algorithms also were given in the form of pseudo program code, and the performance also was evaluated. With the proposed model, the transactions of Key-Value NoSQL databases can be performed in a lock free and MVCC (Multi-Version Concurrency Control) free manner. This is the result of further research on the related topic, which fills the gap ignored by relevant scholars in this field to make a little contribution to the further development of NoSQL technology.

A brief procedure for big data analysis of gene expression

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.

基于大数据的高校学生心理危机智能预警模型构建

目的本研究基于大学生心理健康测评数据,综合学生基本信息及日常行为数据,利用大数据、人工智能技术,探索建立大学生心理危机智能预警模型。方法采用整群抽样法,选取某高校部分在校大学生作为测试样本;采取大数据技术,分析提取影响大学生心理健康问题的特征量;采取神经网络技术,构建大学生心理危机预警模型。结果1)成功提取大学生心理健康问题的有效特征量,其中心理健康症状特征因子4个,分别是强迫、人际关系敏感、抑郁等3个单症状因子,1个多症状因子;学生基本信息特征量4个,分别是母亲教养方式、父亲教养方式、家庭经济条件、有无心理治疗(咨询)史;学生日常行为特征量2个,分别是学生学业情况和出勤情况。2)实现特征量归一化处理,通过数据对比分析及特征量的影响大小,分别对3个方面的10个特征量赋予权重并进行归一化处理。3)完成大学生心理危机预警模型构建。结论本研究提取了影响大学生心理健康问题的主要特征,建立了大学生心理危机预警指标体系,并结合心理危机预警等级,利用神经网络技术,搭建了大学生心理危机预警模型,为及时有效干预大学生心理危机提供了新的解决方案。

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。

大数据背景下工商管理学生数据分析能力素质模型的构建与应用

在大数据时代背景下,企业对工商管理人才的数据分析能力要求提升,这也对高校现有工商管理学生的培养方式提出了挑战。基于访谈、岗位分析和理论分析,工商管理学生应具备数据分析知识和技能、数据思维和数据态度,据此构建数据分析能力素质模型,并建立相应的测量模型和指标体系,包括数据获取能力、数据预处理能力、数据分析能力、数据应用能力、数据思维和数据态度六个方面,运用数据分析能力素质模型能够为高校培养工商管理学生数据分析能力提供理论支持与实践建议。

大数据牵引背景下的服装设计开发模式

在大数据牵引背景下,随着数据量呈爆炸式增长和大数据应用的不断深化,大数据资源以数据要素的形式牵引着传统服装设计模式的改变。通过对服装设计相关的大数据进行分类甄别,探讨大数据在多方面赋能服装设计协同价值创造,多维度比较传统服装设计开发与以大数据牵引的服装设计开发模式,以大数据赋能服装精准设计,构建基于大数据引导下的服装设计框架;将该框架应用于MZ品牌的开发案例中,从商品企划、设计开发和预售下单3个方面论述大数据在服装设计开发中的实践应用。大数据时代设计模式的演变将更加聚焦于设计师与海量数据之间的相互作用,应用大数据牵引服装设计开发模式的迭代,可实现更高精准度和更快反应能力的服装设计开发。

教育大数据背景下高职英语个性化精准教学模式与应用研究

英语学科在高职教育体系中,有提升学生整体素养的作用。而在教育大数据背景下,为了更好地提升英语教学活动的针对性,本文认为应该结合学生实际需求,按照学生不同学情基础,展开个性化教学和精准化教学,利用智慧化网络平台全方位分析、搜集、整合学生在学习阶段产生的教育数据,通过分析结果明确学生学习短板,并结合分层式教学理念调整教学方案,确保英语学科教学的实效性。对此,本文将结合探寻高职英语个性化精准教学模式与应用,希望可以为其他教职员工进行研究提供参考。

教育大模型的发展现状、创新架构及应用展望

从通用大模型到教育大模型,是人工智能大模型技术深化发展的重要趋势。基于对教育大模型发展现状、典型案例、潜在挑战的分析,文章认为教育大模型是适用于教育场景、具有超大规模参数、融合通用知识和专业知识训练形成的人工智能模型,是大模型技术、知识库技术及各类智能教育技术的集成,能够推动人类学习和机器学习的双向建构,进而提出了应用驱动、共建共享的创新架构和“以学习者为中心”的未来应用场景,旨在建立人工智能大模型与各类数字化教育应用的开放接口,持续训练和完善能够更好地解决教育专业问题的教育场景模型,形成让广大师生常态化使用的智能教育开放模型集群和知识库,在提炼和萃取深度教育知识的同时,破解人工智能教育应用中的风险和挑战。