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1.
大数据即网络社会中的巨量资料,在经过技术处理之后能为人们创造价值提供分析和预测的生产要素和信息资源。大数据时代推动着教育的理念革命、方法革命和行为革命,推动着思想政治教育的大数据化。思想政治教育的大数据化,是人们运用信息网络技术来把大数据与思想政治教育相结合,获取、处理和利用思想政治教育领域以及与之相关的巨量资料,分析和预测思想政治教育的现状和态势,推动思想政治教育网络的创新和应用,产生巨大的思想政治教育价值。这需要构建思想政治教育网络,其实质就在于从价值理性、“互联网+”、应用结构、战略管理的具体层面来构建思联网。 相似文献
2.
Big Data Analytics (BDA) is increasingly becoming a trending practice that generates an enormous amount of data and provides a new opportunity that is helpful in relevant decision-making. The developments in Big Data Analytics provide a new paradigm and solutions for big data sources, storage, and advanced analytics. The BDA provide a nuanced view of big data development, and insights on how it can truly create value for firm and customer. This article presents a comprehensive, well-informed examination, and realistic analysis of deploying big data analytics successfully in companies. It provides an overview of the architecture of BDA including six components, namely: (i) data generation, (ii) data acquisition, (iii) data storage, (iv) advanced data analytics, (v) data visualization, and (vi) decision-making for value-creation. In this paper, seven V's characteristics of BDA namely Volume, Velocity, Variety, Valence, Veracity, Variability, and Value are explored. The various big data analytics tools, techniques and technologies have been described. Furthermore, it presents a methodical analysis for the usage of Big Data Analytics in various applications such as agriculture, healthcare, cyber security, and smart city. This paper also highlights the previous research, challenges, current status, and future directions of big data analytics for various application platforms. This overview highlights three issues, namely (i) concepts, characteristics and processing paradigms of Big Data Analytics; (ii) the state-of-the-art framework for decision-making in BDA for companies to insight value-creation; and (iii) the current challenges of Big Data Analytics as well as possible future directions. 相似文献
3.
保障粮食安全是全球可持续发展的基础及重要议题。粮食可持续生产作为实现粮食安全的基础,同时是应对气候变化、土地退化、生态退化等全球挑战的有效手段。当前,对粮食生产可持续性的监测与评估存在着数据鸿沟,而地球大数据的支撑作用日益凸显。文章总结了地球大数据支撑粮食可持续生产研究的当前实践,包括对地观测技术在粮食生产系统各要素监测中发挥的作用,以及多源数据融合在粮食生产系统综合监测及粮食生产可持续性评估中的应用。在上述实践归纳的基础上,依循实现联合国可持续发展目标(SDGs)的四大杠杆框架,提出了地球大数据支撑粮食可持续生产的2个未来发展方向:多学科模型凝聚地球大数据推动知识发现支撑政府治理;技术创新集成地球大数据搭建产农户智慧生产决策体系。 相似文献
4.
数据要素价值化动态过程机制研究 《科学学研究》2022,40(2):220-229
数据作为全新的生产要素，已成为数字经济时代的“石油”，数据要素驱动的创新创业正成为新发展阶段实现高质量发展的新引擎。但鲜有针对数据要素价值化过程及其价值创造机制的研究。本研究针对新发展格局下数据要素价值化的难题，结合数据要素的“5I”社会属性，建构“要素-机制-绩效”这一过程视角下数据要素价值化的动态整合理论模型，系统论述通过数据银行实现数据要素多维价值创造的五阶段动态过程机制，也即低成本汇聚、规范化确权、高效率治理、资产化交易和全场景应用，讨论了落实这一机制的现实挑战和政策建议。本研究为打开数据要素价值化过程“黑箱”，加速数据要素价值化、赋能数字产业化和产业数字化，进而实现数字创新引领新发展阶段的高质量可持续发展提供理论与实践启示。 相似文献
5.
《国家科学评论(英文版)》2014,(2)
Big Data bring new opportunities to modern society and challenges to data scientists. On the one hand,Big Data hold great promises for discovering subtle population paterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage botleneck, noise accumulation, spurious correlation, incidental endogeneity and measurement errors.hese challenges are distinguished and require new computational and statistical paradigm. his paper gives overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasize on the viability of the sparsest solution in high-conidence set and point out that exogenous assumptions in most statistical methods for Big Data cannot be validated due to incidental endogeneity. hey can lead to wrong statistical inferences and consequently wrong scientiic conclusions. 相似文献
6.
Over recent years, organizations have started to capitalize on the significant use of Big Data and emerging technologies to analyze, and gain valuable insights linked to, decision-making processes. The process of Competitive Intelligence (CI) includes monitoring competitors with a view to delivering both actionable and meaningful intelligence to organizations. In this regard, the capacity to leverage and unleash the potential of big data tools and techniques is one of various significant components of successfully steering CI and ultimately infusing such valuable knowledge into CI strategies. In this paper, the authors aim to examine Big Data applications in CI processes within organizations by exploring how organizations deal with Big Data analytics, and this study provides a context for developing Big Data frameworks and process models for CI in organizations. Overall, research findings have indicated a preference for a rather centralized informal process as opposed to a clear formal structure for CI; the use of basic tools for queries, as opposed to reliance on dedicated methods such as advanced machine learning; and the existence of multiple challenges that companies currently face regarding the use of big data analytics in building organizational CI. 相似文献
7.
大数据浪潮在全球范围内呈愈演愈烈的趋势。既有的隐私乱象在灵活多变的大数据影响下,会受到更多的挑战,但同时,大数据也为个人隐私的妥善处理与保护带来了多种可能,危机与机遇并存。全文从新的视角出发,运用哲学的思维,采取以定性论述为主,定量建模为辅的方法,重新探讨信息的时效性,并针对现有的隐私问题逐一进行探究,并分别提出相应的对策。隐私问题不仅关乎个人,更关乎国家,良好的隐私意识和智慧保护技术都将保证现有的隐私问题最终得以妥善解决。 相似文献
8.
大数据时代,人们正在以“分析全样本、接收非精确、发现相关性”的新思维探索世界。相应的技术手段日渐成熟,包含大数据处理系统、新型知识服务模式、智能决策支持的大数据科研服务平台有望成为科研新工具。新技术结合新理念,大数据正在加速科学发现、凝聚科学共同体、改变知识生产模式,数据密集型科学有可能成为科研“第四范式”。为了获取新一轮科技竞争优势、提高社会生产力,大数据将在科技政策中占有重要地位,不过,也要防范大数据的负面影响。 相似文献
9.
大数据在地球科学各个学科中的应用越来越受到关注,数据驱动地球科学发现的案例不断出现,有关地球数据信息中心、地球大数据平台及相关学术会议数量逐渐增加,地球大数据正在科学研究上表现出巨大的潜力。科学家对地球大数据的科学方法和工具的需求很大,然而目前地球大数据的理论基础、储存管理和分析方法等仍处于发展之中,对地球大数据的研究和讨论有限。文章通过文献计量学的方法,对科学引文索引(SCI)和社会科学引文索引(SSCI)收录的地球大数据相关文献进行分析,从全球论文的产出数量、国家与机构领域研究影响力、研究主题分布、研究热点变迁和国际合作等多角度,分析揭示了地球大数据研究现状;最后,建议未来重点加强跨学科的地球大数据共享与融合,完善地球科学大数据深度挖掘理论和方法,实现对复杂地球系统的分析、建模与预测,支持和服务全球变化与可持续发展。 相似文献
10.
Energy efficiency of public sector is an important issue in the context of smart cities due to the fact that buildings are the largest energy consumers, especially public buildings such as educational, health, government and other public institutions that have a large usage frequency. However, recent developments of machine learning within Big Data environment have not been exploited enough in this domain. This paper aims to answer the question of how to incorporate Big Data platform and machine learning into an intelligent system for managing energy efficiency of public sector as a substantial part of the smart city concept. Deep neural networks, Rpart regression tree and Random forest with variable reduction procedures were used to create prediction models of specific energy consumption of Croatian public sector buildings. The most accurate model was produced by Random forest method, and a comparison of important predictors extracted by all three methods has been conducted. The models could be implemented in the suggested intelligent system named MERIDA which integrates Big Data collection and predictive models of energy consumption for each energy source in public buildings, and enables their synergy into a managing platform for improving energy efficiency of the public sector within Big Data environment. The paper also discusses technological requirements for developing such a platform that could be used by public administration to plan reconstruction measures of public buildings, to reduce energy consumption and cost, as well as to connect such smart public buildings as part of smart cities. Such digital transformation of energy management can increase energy efficiency of public administration, its higher quality of service and healthier environment. 相似文献
11.
Andrea De Mauro Marco Greco Michele Grimaldi Paavo Ritala 《Information processing & management》2018,54(5):807-817
The rapid expansion of Big Data Analytics is forcing companies to rethink their Human Resource (HR) needs. However, at the same time, it is unclear which types of job roles and skills constitute this area. To this end, this study pursues to drive clarity across the heterogeneous nature of skills required in Big Data professions, by analyzing a large amount of real-world job posts published online. More precisely we: 1) identify four Big Data ‘job families’; 2) recognize nine homogeneous groups of Big Data skills (skill sets) that are being demanded by companies; 3) characterize each job family with the appropriate level of competence required within each Big Data skill set. We propose a novel, semi-automated, fully replicable, analytical methodology based on a combination of machine learning algorithms and expert judgement. Our analysis leverages a significant amount of online job posts, obtained through web scraping, to generate an intelligible classification of job roles and skill sets. The results can support business leaders and HR managers in establishing clear strategies for the acquisition and the development of the right skills needed to leverage Big Data at best. Moreover, the structured classification of job families and skill sets will help establish a common dictionary to be used by HR recruiters and education providers, so that supply and demand can more effectively meet in the job marketplace. 相似文献
12.
大数据挖掘为经济和社会问题研究提供了崭新方法,但对隐私权在内的个人基本权利的潜在侵犯风险不容忽视。归纳大数据挖掘所面临的隐私风险问题,探讨隐私保护数据分析的流程及策略,从数据格式、知识产权、服务条款、社交网络等方面指出网络环境下隐私保护的技术趋势,并就立法完善提出建议。 相似文献
13.
联合国《改变我们的世界:2030年可持续发展议程》是各国实现经济、社会和环境共同发展的重要指南。当前,该议程的17个可持续发展目标(SDGs)的监测和评价已取得重要进展,但各SDGs间相互作用,特别是SDGs间的协同和权衡关系的认知仍较有限。文章首先从全部目标关系的综合分析、典型多目标关系分析、单目标内子指标间的关系3个方面描述了当前SDGs协同与权衡的研究进展和主要发现;并针对研究中的数据瓶颈问题,剖析了地球大数据支撑多目标协同和权衡的思路及典型案例;在此基础上,对地球大数据促进SDGs协同和权衡研究进行了展望。研究表明,地球大数据在提升SDG指标数据一致性、透明性、时效性和准确性等方面能够发挥重要作用,可以改进前期基于专家知识或统计数据等方法的不足,为提升多目标协同和权衡研究的定量水平提供重要数据支撑。最后,应对SDGs权衡的挑战,提出了完善地球大数据支撑SDGs协同与权衡的方法体系并构建模拟与预警平台、加强不同领域和主体的合作、强化技术创新推动等建议。 相似文献
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15.
《Information processing & management》2023,60(3):103326
The traditional Management Information System (MIS) with Big Financial Data (BFD) for corporate financial diagnosis has many limitations such as the data is not summarized thus these causing increases in query times, and also the complexity in analysis. The creation of a Data Mart (DM) leads to a great summarization of data, such that contains only essential business information. And by using data mining techniques we can be extracting unknown useful information from DM and apply it to make important decisions for the business. Thus, in this paper we are adopting an architecture of six layers; interface layer, analysis layer, extract transformation load layer, data mart layer, data mining layer, and evaluating layer, MIS with BFD using DM and Mining (MIS-BFD-DMM) is proposed, which is not only permits the use of DM and mining technologies in decision support, but also the full utilization of non-financial/financial info held by businesses. This paper offers the benefits of building and integrating DM with mining. Also determines the distinction between DM and a relational database for decision-makers to get information. The test and analysis are achieved in the terms of useful metrics (accuracy, balance accuracy, F-measure, precision, recall, and time). As a result, Data returned from arranged star schema is far faster than ERD. In conclusion, the SVM is best than other algorithms in terms of the parameters of the confusion matrix. 相似文献
16.
信息技术的发展使企业从多个源头捕获海量数据成为可能,大数据作为新型资产是企业获得竞争优势的重要元素。目前学术界对于大数据如何影响现有的创新管理理论和企业的创新管理实践并未做系统性论述。本文通过文献回顾和案例分析,结合大数据的特征,从大数据对企业创新模式、创新参与主体、创新战略以及创新组织四个方面分别进行探讨,认为大数据背景下企业创新管理将呈现迭代创新、平台战略、海量用户参与的趋势。文章提出了大数据对创新管理范式影响的概念框架,为大数据时代的企业创新实践提供思路与借鉴。 相似文献
17.
Artificial intelligence (AI) has been in existence for over six decades and has experienced AI winters and springs. The rise of super computing power and Big Data technologies appear to have empowered AI in recent years. The new generation of AI is rapidly expanding and has again become an attractive topic for research. This paper aims to identify the challenges associated with the use and impact of revitalised AI based systems for decision making and offer a set of research propositions for information systems (IS) researchers. The paper first provides a view of the history of AI through the relevant papers published in the International Journal of Information Management (IJIM). It then discusses AI for decision making in general and the specific issues regarding the interaction and integration of AI to support or replace human decision makers in particular. To advance research on the use of AI for decision making in the era of Big Data, the paper offers twelve research propositions for IS researchers in terms of conceptual and theoretical development, AI technology-human interaction, and AI implementation. 相似文献
18.
科研大数据共生作为科研大数据共享的重要过程,在科研数据生成过程中发挥着至关重要的作用,探究其内在机理具有重要的理论价值和实践价值。文章在共生理论的基础上,给出"科研大数据共生"的概念,构建了科研大数据共生模型(SM-SRBD),然后从维度分析、共生方程分析与寄生机制的关联分析等几个方面,深入阐释了科研大数据共生的内在运行机理,分析了科研大数据共生和寄生之间的趋利型和趋害型两类演化路径。研究表明:科研大数据共生是一个以利益维、自由度维、空间维、时间维、强度维等不同维度的共生活动为核心活动体系,以"共生数据源"为基,以"数据共生方程"为过程逻辑,以实现优势互补、共同成长(或偏利成长)并生成共生化新数据、构建科研大数据命运共同体、提升科研大数据质量为目标,不断趋于优化的泛在化、协同化的动态进化过程。 相似文献
19.
美国政府NIST大数据互操作性框架的特点研究及启示 总被引：1，自引：0，他引：1
[目的/意义] 对美国政府大数据互操作性框架提出的背景、具体内容和主要特点进行分析与总结,以期为我国制定大数据参考框架、促进跨界合作提供有益的参考。[方法/过程] 以内容分析法和文本分析法为主要研究方法,以从美国NIST官网获得的公开政策、研究报告等作为主要数据来源,从数据层、框架层、角色层和应用层等方面分析总结美国大数据参考框架的特点。[结果/结论] 分析发现：NIST构建了一个具有较强参考性与适用性的大数据概念框架,着重体现了大数据范式的前后变化并鼓励挖掘大数据应用的可能性。启示我国政府在制定大数据参考框架时,应当在理论层面达成共识的前提下,关注可参考价值与利益相关者的开发需求,同时在需求与价值之间构建起映射关系。 相似文献
20.
《International Journal of Information Management》2017,37(6):750-760
People, devices, infrastructures and sensors can constantly communicate exchanging data and generating new data that trace many of these exchanges. This leads to vast volumes of data collected at ever increasing velocities and of different variety, a phenomenon currently known as Big Data. In particular, recent developments in Information and Communications Technologies are pushing the fourth industrial revolution, Industry 4.0, being data generated by several sources like machine controllers, sensors, manufacturing systems, among others. Joining volume, variety and velocity of data, with Industry 4.0, makes the opportunity to enhance sustainable innovation in the Factories of the Future. In this, the collection, integration, storage, processing and analysis of data is a key challenge, being Big Data systems needed to link all the entities and data needs of the factory. Thereby, this paper addresses this key challenge, proposing and implementing a Big Data Analytics architecture, using a multinational organisation (Bosch Car Multimedia – Braga) as a case study. In this work, all the data lifecycle, from collection to analysis, is handled, taking into consideration the different data processing speeds that can exist in the real environment of a factory (batch or stream). 相似文献