Using Python to Analyze Financial Big Data

As technology and the internet develop,more data are generated every day.These data are in large sizes,high dimensions,and complex structures.The combination of these three features is the“Big Data”[1].Big data is revolutionizing all industries,bringing colossal impacts to them[2].Many researchers have pointed out the huge impact that big data can have on our daily lives[3].We can utilize the information we obtain and help us make decisions.Also,the conclusions we drew from the big data we analyzed can be used as a prediction for the future,helping us to make more accurate and benign decisions earlier than others.If we apply these technics in finance,for example,in stock,we can get detailed information for stocks.Moreover,we can use the analyzed data to predict certain stocks.This can help people decide whether to buy a stock or not by providing predicted data for people at a certain convincing level,helping to protect them from potential losses.

Digital twin intelligent system for industrial internet of things-based big data management and analysis in cloud environments

This work surveys and illustrates multiple open challenges in the field of industrial Internet of Things(IoT)-based big data management and analysis in cloud environments.Challenges arising from the fields of machine learning in cloud infrastructures,artificial intelligence techniques for big data analytics in cloud environments,and federated learning cloud systems are elucidated.Additionally,reinforcement learning,which is a novel technique that allows large cloud-based data centers,to allocate more energy-efficient resources is examined.Moreover,we propose an architecture that attempts to combine the features offered by several cloud providers to achieve an energy-efficient industrial IoT-based big data management framework(EEIBDM)established outside of every user in the cloud.IoT data can be integrated with techniques such as reinforcement and federated learning to achieve a digital twin scenario for the virtual representation of industrial IoT-based big data of machines and room tem-peratures.Furthermore,we propose an algorithm for determining the energy consumption of the infrastructure by evaluating the EEIBDM framework.Finally,future directions for the expansion of this research are discussed.

Cyber Resilience through Real-Time Threat Analysis in Information Security

This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].

Government Management Innovation in the Age of Big Data

Both opportunities and challenges are currently faced by government management innovation in the age of ＂big data＂. Traditionally, relative studies view the management of governments as the effective means to improve governmental services, without really understanding the structural influence of big data and network technology on governmental mode of thinking. Against such backdrop, this paper tries to conduct critical analysis based upon traditional outcomes in this regard, trying to make full use of the function of big data technology. With these efforts, this paper contributes to the building of an interaction theory that could promote transparency of information and customization and segmentation of the policies. By constructing a mode in which management could be carried out based on the law of big data, by building an information management system in which balance could be achieved between responsibility and freedom, by promoting the rebalancing among public power, online civil society and civil rights, the innovation of governmental management would be achieved.

Design of a Student Recommendation Platform Based on Learning Behavior and Habit Training

This study innovatively built an intelligent analysis platform for learning behavior,which deeply integrated the cutting-edge technology of big data and Artificial Intelligence(AI),\mined and analyzed students’learning data,and realized the personalized customization of learning resources and the accurate matching of intelligent learning partners.With the help of advanced algorithms and multi-dimensional data fusion strategies,the platform not only promotes positive interaction and collaboration in the learning environment but also provides teachers with comprehensive and in-depth students’learning portraits,which provides solid support for the implementation of precision education and the personalized adjustment of teaching strategies.In this study,a recommender system based on user similarity evaluation and a collaborative filtering mechanism is carefully designed,and its technical architecture and implementation process are described in detail.

Data mining in clinical big data:the frequently used databases,steps,and methodological models

Many high quality studies have emerged from public databases,such as Surveillance,Epidemiology,and End Results(SEER),National Health and Nutrition Examination Survey(NHANES),The Cancer Genome Atlas(TCGA),and Medical Information Mart for Intensive Care(MIMIC);however,these data are often characterized by a high degree of dimensional heterogeneity,timeliness,scarcity,irregularity,and other characteristics,resulting in the value of these data not being fully utilized.Data-mining technology has been a frontier field in medical research,as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models.Therefore,data mining has unique advantages in clinical big-data research,especially in large-scale medical public databases.This article introduced the main medical public database and described the steps,tasks,and models of data mining in simple language.Additionally,we described data-mining methods along with their practical applications.The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.

Design and Implementation of Book Recommendation Management System Based on Improved Apriori Algorithm

The traditional Apriori applied in books management system causes slow system operation due to frequent scanning of database and excessive quantity of candidate item-sets, so an information recommendation book management system based on improved Apriori data mining algorithm is designed, in which the C/S (client/server) architecture and B/S (browser/server) architecture are integrated, so as to open the book information to library staff and borrowers. The related information data of the borrowers and books can be extracted from books lending database by the data preprocessing sub-module in the system function module. After the data is cleaned, converted and integrated, the association rule mining sub-module is used to mine the strong association rules with support degree greater than minimum support degree threshold and confidence coefficient greater than minimum confidence coefficient threshold according to the processed data and by means of the improved Apriori data mining algorithm to generate association rule database. The association matching is performed by the personalized recommendation sub-module according to the borrower and his selected books in the association rule database. The book information associated with the books read by borrower is recommended to him to realize personalized recommendation of the book information. The experimental results show that the system can effectively recommend book related information, and its CPU occupation rate is only 6.47% under the condition that 50 clients are running it at the same time. Anyway, it has good performance.

Intelligent Biometric Information Management

We advance here a novel methodology for robust intelligent biometric information management with inferences and predictions made using randomness and complexity concepts. Intelligence refers to learning, adap- tation, and functionality, and robustness refers to the ability to handle incomplete and/or corrupt adversarial information, on one side, and image and or device variability, on the other side. The proposed methodology is model-free and non-parametric. It draws support from discriminative methods using likelihood ratios to link at the conceptual level biometrics and forensics. It further links, at the modeling and implementation level, the Bayesian framework, statistical learning theory (SLT) using transduction and semi-supervised lea- rning, and Information Theory (IY) using mutual information. The key concepts supporting the proposed methodology are a) local estimation to facilitate learning and prediction using both labeled and unlabeled data;b) similarity metrics using regularity of patterns, randomness deficiency, and Kolmogorov complexity (similar to MDL) using strangeness/typicality and ranking p-values;and c) the Cover – Hart theorem on the asymptotical performance of k-nearest neighbors approaching the optimal Bayes error. Several topics on biometric inference and prediction related to 1) multi-level and multi-layer data fusion including quality and multi-modal biometrics;2) score normalization and revision theory;3) face selection and tracking;and 4) identity management, are described here using an integrated approach that includes transduction and boosting for ranking and sequential fusion/aggregation, respectively, on one side, and active learning and change/ outlier/intrusion detection realized using information gain and martingale, respectively, on the other side. The methodology proposed can be mapped to additional types of information beyond biometrics.

个人征信行业的创新方向——韩国MyData行业与征信应用

个人征信体系作为消费经济的基础设施,在数字经济时代的地位越来越重要。传统个人征信模式面临新的数字经济形态、科技创新、个人数据严监管以及普惠金融的冲击,需要与时俱进。在韩国,“本人数据管理”(MyData)模式的创新尝试给个人征信带来了创新方向,有助于解决国内征信数据断直连监管。根据MyData模式的基本理念、发展路径和实践案例,从征信行业发展、数据合规和征信业务推进等方面总结启示。

基于大数据技术的企业信息资源管理系统

企业信息资源管理系统面对大规模和多类型数据涌入时,处理效率低,系统容易崩溃,设计基于大数据技术的企业信息资源管理系统。分析Hadoop平台体系结构,确定资源管理系统整体架构,设计每个子系统对应的功能,部署系统硬件环境;利用大数据融合技术设置软件执行算法,通过模糊调度挖掘资源间关联规则,根据相似性函数,建立同类资源的融合模型,提高业务处理效率,完成系统整体设计。实验表明,该系统的CPU利用率仅在30%左右,处理时间在1.00s以内,大大缩短了处理时间,提高了处理效率。

大数据公共管理情境下的个人信息披露态度

文章在大数据公共管理背景下,以新冠疫情防控期间的个人信息披露态度为例,研究个人内在因素和外在环境因素对个人信息披露态度的影响,以期为建构大数据公共管理中个人信息和谐的“披露-采集”关系提供理论支撑。研究设计综合保护动机理论和调节定向理论,引入隐私顾虑作为中介变量,构建结构方程模型;通过问卷调查法,收集覆盖16个城市4,800份大规模样本进行分析。研究发现:自我效能负向影响隐私顾虑,主观规范正向影响隐私顾虑,隐私顾虑负向影响个人信息披露态度;促进定向个体比预防定向个体隐私顾虑水平更低,更倾向于支持公共管理中的个人信息披露,同时调节定向特质在自我效能与隐私顾虑之间具有显著调节效应。

大数据背景下的电力营销信息管理平台设计

为了提升电力营销大数据处理和调度效率,设计大数据背景下的电力营销信息管理平台。基于Hadoop和MapReduce设计平台存储和技术框架,利用大数据技术划分电力营销大数据,实现电力营销信息状态安全分析和监控;采用云计算技术分析和处理电力营销数据。测试结果显示,所设计平台的大数据处理吞吐量较高、大数据处理加速比稳定;数据调度计算效率较高、性能稳定;将电力营销数据以对象形式缓存到内存中时,执行时间较短、耗费内存较小。该平台具备较好的数据处理及调度能力。

我国数据要素研究的文献计量与可视化

[目的/意义]数据要素作为关键生产要素,对于推动我国经济社会发展具有重要意义。通过对数据要素研究的可视化分析,旨在为政策制定者、企业管理者和学者提供有关数据要素研究的全面认识,以期为数据要素的发展和应用提供智力支持。[方法/过程]选择了中国知网(CNKI)数据库作为主要的文献检索平台,以“数据要素”“数据要素市场”“数据生产要素”“数据交易”为关键词进行检索。在获取相关文献后,对相关文献的年度发文量趋势、作者和机构的发文分布情况等进行了分析。此外,通过VOSviewer软件绘制了图谱,以直观地展现研究领域的研究主题和演变趋势。[结果/结论]研究发现,数据要素研究在我国已经取得了丰硕的成果,发文量逐年上升,研究热度不断攀升。数据要素研究涉及多个学科,如经济学、法学和管理学等,研究方法日趋多样化。根据关键词共现分析和关键词时区图分析,数据要素和数字经济是研究的核心概念,研究不仅丰富了理论框架,还对实践应用产生了影响,为政策制定和行业实践提供了重要的指导和支持。

大数据时代智慧农业实验室管理创新策略研究

大数据时代背景下,高校智慧农业实验室作为农业教育与科研的关键平台,面临管理革新需求。当前,这些实验室普遍采用的传统管理系统与智慧农业的快速发展不相匹配,限制了其潜能发挥。本文针对性地分析了智慧农业实验室的管理现状,并依托大数据技术,探索创新管理模式,旨在通过信息技术、智慧化及移动管理工具集成,优化资源配置与提升管理效率。提出的策略旨在最大化实验室资源效用,加速智慧农业教育与研究进程,推动该领域的持续创新与进步。

大数据时代医院档案管理信息化建设的思考

本文以大数据时代医院档案管理信息化建设的积极价值为切入点,在此基础上探讨当前该工作的不足以及应对策略,就确保覆盖效应、保证系统延伸性、重视安全隐患控制等内容做具体论述,以改善大数据时代医院档案管理信息化建设水平,为其医疗服务、区域卫生管理等工作提供参考。

从消费者权到个人信息保护:大数据杀熟的私法规制

大数据杀熟有赖于对个人信息的获取和利用,从传统的消费者权利保护角度进行规制存在明显的局限。而个人信息保护路径,即从个人信息保护的角度出发,通过有效控制个人信息提前阻断大数据杀熟的产生,可以在很大程度上弥补消费者权利保护路径的不足。我国《个人信息保护法》为这一路径提供了法律依据和基本框架。该法第24条第一款为规制大数据杀熟的中心条款,大数据杀熟属于该条款所指的“不合理的差别待遇”。在实践中,可以从事前预防和事后救济两个阶段对大数据杀熟予以规制。就事前预防而言,该法为网络平台处理用户个人信息的行为划定了界限,大数据杀熟属于不当处理个人信息的违法行为。同时,个人信息提供者对算法推荐服务享有拒绝权,可以有效防范个人信息授权制度被滥用于大数据杀熟的风险。就事后救济而言,网络平台只要存在这种违法行为,就需承担相应的民事责任,包括违反个人信息授权合同的违约责任和侵害个人信息权益的侵权责任。

数字经济时代个人信息保护司法实践探析

数字化经济是实现中国式现代化的重要举措,为社会经济发展带来重大机遇,也为个人信息保护带来诸多新挑战。司法治理作为数字经济治理体系不可或缺的重要组成部分,在个人信息保护领域发挥着积极作用。当前,司法实践遭受数字经济的冲击,呈现法律边界划定模糊、法律文本适用争议和特殊群体保护缺位等局限。需要通过在法律边界空白领域适当扩大法律渊源、在法律适用时允许适度的法官造法和完善特殊群体个人信息保护政策机制来弥合传统司法实践与现实困境的鸿沟。

大数据背景下个人生物信息法律保护研究

加强个人生物信息的法律保护,可以从注重和保障人权、维护个人隐私、保障公共利益、促进经济与科技发展等维度应对生物信息技术应用中所面临的法律风险。在大数据背景下,我们可以结合个人生物信息的概念、特征及权益性质,从司法救济、加强行政监管及法律保护规范等角度进行法律保护,以完善传统个人生物信息法律保护的不足,维护权益人的个人权益、社会公益及伦理道德。

大数据时代国内外个人信息保护研究热点和演化趋势——基于科学知识图谱分析的文献计量方法分析

[目的/意义]对大数据时代国内外个人信息保护的研究热点和演化趋势进行了总结和回顾,旨在为相关领域的研究提供参考和启示。[方法/过程]运用文献计量法和科学知识图谱法,基于CNKI和Web of Science数据库,以ITGInsight为主体工具,再辅之Gephi、Excel、SATI等科学计量与知识网络分析软件,对大数据领域国内外个人信息保护研究领域的热点分布、主题演化以及研究内容进行分析。[结果/结论]大数据时代国内外个人信息保护相关研究主题分布广泛、演化规律较为复杂,呈现出显著的变化趋势,在未来的研究中,需要综合考虑技术、法律、政策等多个方面的因素,以构建更加全面、系统的个人信息保护体系。

高职院校大数据驱动下的精细化教学研究

随着人工智能和大数据等新兴技术的发展,作为一种基于学生个体差异的教学方式,基于大数据驱动的精细化教学在高职院校中逐渐成为推进教育改革和提高教学质量的重要手段。文章首先介绍教育大数据的内涵及其对教学精细化的推动作用。然后阐述教育大数据驱动教学精细化的实现过程。并从提高教学质量、优化学习过程等方面探讨教育大数据驱动教学精细化的意义。最后指出在推进教育大数据驱动教学精细化过程中面临的问题和挑战。