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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
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联合国2015年通过的《变革我们的世界:2030年可持续发展议程》中,土地退化零增长是可持续发展目标(SDGs)的重要具体目标(SDG 15.3)。由于不同地理、气候和土地利用类型条件下土地退化具有不同的表征,土地退化和恢复过程涉及的自然和人为因素的复杂性,时间和空间尺度的限定性,长期以来缺乏被普遍接受的土地退化评估指标和方法。SDG 15.3的参考基准、进展监测等关键数据,仍处于严重缺乏状态,影响了SDG 15.3的实现进程。地球大数据作为数据密集型科学范式的典型代表,为解决SDG 15.3的数据空缺提供了可能。文章围绕SDG 15.3基准确定和进展监测2个方向,介绍了实现SDG 15.3面临的主要挑战、地球大数据的潜力及已经开展的实践,并展望了地球大数据促进SDG 15.3实现的发展趋势。 相似文献
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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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"可持续城市和社区"(SDG 11)是实现所有17项联合国可持续发展目标(SDGs)的核心。然而,普遍存在的数据缺失问题导致目前SDG 11指标监测与评估工作的开展仍面临巨大挑战。地球大数据作为科技创新和大数据的重要组成部分,在促进城市可持续发展方面能够发挥关键作用。文章重点围绕城市可持续发展的6个主题,包括城市住房、城市公共交通、城镇化、城市灾害、空气质量、开放公共空间,基于地球大数据技术,针对相应的多个具体指标,在中国尺度上开展进展监测和综合评估。在上述分析的基础上,文章总结了SDG 11实现面临的挑战;并提出了构建可持续发展大数据信息平台、加强科学技术在SDG 11实现中的杠杆作用、积极开展SDG 11综合应用示范,以及加强国内外相关机构的科技合作等建议和举措。 相似文献
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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
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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
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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
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城市的可持续发展是实现联合国2030年可持续发展目标(SDGs)的关键。地球大数据对于克服城市可持续发展评估中传统统计数据的不足具有重要意义,它能够凭借宏观、动态、多样的优势为城市可持续发展评估提供新的动力。文章在系统梳理国内外城市可持续性评价指标研究与评估实践的基础上,以SDG 11(可持续城市与社区)为核心,兼顾其他SDGs中体现城市可持续发展的维度,充分挖掘地球大数据完备、实时、稳健、客观等优势,选取12个适合运用地球大数据来进行评估的指标。其中,7个直接指标从SDG11具体标中抽取,依次为SDG 11.1、SDG 11.2、SDG 11.3、SDG 11.6、SDG 11.7、SDG 11.a、SDG 11.b等;另外5个关联指标为不包含在SDG 11中,但与城市可持续评估息息相关的代表性评价指标,分别为SDG6.3、SDG 7.2、SDG 8.1、SDG 9c、SDG 15.1等。文章介绍这12个指标在城市可持续评估中可发挥的作用,以及评估的数据、指标与方法。最后,提出基于地球大数据进行可持续指标计算,实现了对多源信息的整合利用,将有助于实现更加定量、实时、精细的城市可持续评价。 相似文献
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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
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2015年,联合国通过17项可持续发展目标(SDGs),涵盖经济、社会、环境三大领域,其为各国全面转向可持续发展指明方向。然而,数据缺失、发展不均衡、目标间关联且相互制约等问题对于SDGs落实造成制约,2020年全球新冠肺炎疫情的暴发更加剧了各国实现SDGs面临的挑战。文章重点介绍中国科学院战略性先导科技专项(A类)"地球大数据科学工程"(CASEarth)开展的可持续发展科学卫星、可持续发展大数据信息平台系统、SDG指标监测与评估等研究工作,并对可持续发展大数据国际研究中心的科学定位、核心任务及研究内容等进行介绍。文章提出了需提升SDGs数据服务能力,加强SDG指标监测与评估科学研究,研发SDGs科学系列卫星,建设科技创新促进可持续发展智库,以及提供面向发展中国家的教育和培训等发展建议。 相似文献
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海洋生态系统的健康和安全,直接关系到全人类的健康和福祉。有效监测数据不足、科学决策信息缺失等因素一定程度上影响了海洋可持续发展目标(SDG 14)的顺利实施。地球大数据具备宏观、动态、客观监测能力,可在支撑SDG 14实现中起到重要作用。在中国科学院战略性先导科技专项(A类)的支持下,基于地球大数据相关技术和方法,我国已有效开展了海洋缺失数据集生产、目标本地化模型构建等具体实践。在以上分析基础上,文章提出了积极参与国际社会地球大数据共享,加强科技创新对SDG 14实现的驱动,深度参与联合国海洋治理计划等建议。 相似文献
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Industry 4.0 and the associated IoT and data applications are evolving rapidly and expand in various fields. Industry 4.0 also manifests in the farming sector, where the wave of Agriculture 4.0 provides multiple opportunities for farmers, consumers and the associated stakeholders. Our study presents the concept of Data Sharing Agreements (DSAs) as an essential path and a template for AI applications of data management among various actors. The approach we introduce adopts design science principles and develops role-based access control based on AI techniques. The application is presented through a smart farm scenario while we incrementally explore the data sharing challenges in Agriculture 4.0. Data management and sharing practices should enforce defined contextual policies for access control. The approach could inform policymaking decisions for role-based data management, specifically the data-sharing agreements in the context of Industry 4.0 in broad terms and Agriculture 4.0 in specific. 相似文献
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基于盐湖产业发展需求,构建中国盐湖产业大数据平台,形成数据共享与分析决策体系,实现中国盐湖产业数据链全覆盖,辅助科学决策。从盐湖产业生态链闭环角度整合九大数据资源,展开中国盐湖产业大数据平台的设计与实施,打造了基础设施层、数据中心层和平台应用层3个层次的总体架构,并针对盐湖产业数据中心和平台功能建设过程中关键技术及问题难点,提出切实可行的建设方案。平台提供了智能检索、统计分析、专题报告和知识产权导航等服务,为中国盐湖产业转型升级发展提供有价值的参考,是大数据环境下产业大数据平台建设的重要应用示范。 相似文献
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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
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《International Journal of Information Management》2016,36(4):645-652
The internet of things (IoT) is potentially interconnecting unprecedented amounts of raw data, opening countless possibilities by two main logical layers: become data into information, then turn information into knowledge. The former is about filtering the significance in the appropriate format, while the latter provides emerging categories of the whole domain. This path of the data is a bottom-up flow. On the other hand, the path of the process is a top-down flow, starting at the strategic level of business and scientific institutions. Today, the path of the process treasures a sizeable amount of well-known methods, architectures and technologies: the so called Big Data. On the top, Big Data analytics aims variable association (e-commerce), data mining (predictive behaviour) or clustering (marketing segmentation). Digging the Big Data architecture there are a myriad of enabling technologies for data taking, storage and management. However the strategic aim is to enhance knowledge with the appropriate information, which does need of data, but not vice versa. In the way, the magnitude of upcoming data from the IoT will disrupt the data centres. To cope with the extreme scale is a matter of moving the computing services towards the data sources. This paper explores the possibilities of providing many of the IoT services which are currently hosted in monolithic cloud centres, moving these computing services into nano data centres (NaDa). Particularly, data-information processes, which usually are performing at sub-problem domains. NaDa distributes computing power over the already present machines of the IP provides, like gateways or wireless routers to overcome latency, storage cost and alleviate transmissions. Large scale questionnaires have been taken for 300 IT professionals to validate the points of view for IoT adoption. Considering IoT is by definition connected to the Internet, NaDa may be used to implement the logical low layer architecture of the services. Obviously, such distributed NaDa send results on a logical high layer in charge of the information-knowledge turn. This layer requires the whole picture of the domain to enable those processes of Big Data analytics on the top. 相似文献
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《国际创新研究学报(英文)》2020,4(3):76-84
Industry 4.0, known as the fourth technological transformation towards digital-physical systems in manufacturing, creates a disruptive impact on industries. Manufacturing companies, especially small and medium-sized ones, are facing various challenges and must constantly innovate to remain competitive. One way to innovate is by implementing new technologies into company processes. In this study, we investigate how technology, company and industry related factors are associated with the implementation of Industry 4.0 in SMEs. We collect data via a survey with a focus on Industry 4.0 in SMEs. The results indicate that knowledge and expected benefits of technology are the drivers for the implementation of Industry 4.0 technologies. They also show that companies with high levels of process automation and high product variety are more likely to implement Industry 4.0 technologies. Our study creates a better understanding of the status, challenges and plans within Industry 4.0 implementation in SMEs, which will support the development of SME-friendly manufacturing tools and systems and craft managers’ and policymakers’ understanding of Industry 4.0 technologies. 相似文献
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"清洁饮水和卫生设施"(SDG 6)是联合国17个可持续发展目标之一,但截至目前,世界并未走在实现SDG 6的正确轨道上。为了改变这种状况并重新带领世界走上实现SDG 6的道路,联合国倡议并启动了包括融资、数据和信息、能力发展、创新、治理5个方面内容的"SDG 6全球加速框架"。文章从服务于SDG 6指标监测评估的数据和信息角度,分析了当前全球范围内的数据进展、地球大数据技术在SDG 6指标监测评估中的应用情况,总结了全球SDG 6监测评估中存在的2个方面问题:(1)仍缺乏可持续生产的高精度指标数据集;(2)缺乏集数据获取、指标计算、目标评估为一体的运行化系统。在此基础上,提出了建立面向SDG6全目标指标体系监测评估的标准化统计报表与技术指南,以及搭建系统平台的建议。 相似文献
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Since changes in job characteristics in areas such as Industry 4.0 are rapid, fast tool for analysis of job advertisements is needed. Current knowledge about competencies required in Industry 4.0 is scarce. The goal of this paper is to develop a profile of Industry 4.0 job advertisements, using text mining on publicly available job advertisements, which are often used as a channel for collecting relevant information about the required knowledge and skills in rapid-changing industries. We searched website, which publishes job advertisements, related to Industry 4.0, and performed text mining analysis on the data collected from those job advertisements. Analysis of the job advertisements revealed that most of them were for full time entry; associate and mid-senior level management positions and mainly came from the United States and Germany. Text mining analysis resulted in two groups of job profiles. The first group of job profiles was focused solely on the knowledge related to Industry 4.0: cyberphysical systems and the Internet of things for robotized production; and smart production design and production control. The second group of job profiles was focused on more general knowledge areas, which are adapted to Industry 4.0: supply change management, customer satisfaction, and enterprise software. Topic mining was conducted on the extracted phrases generating various multidisciplinary job profiles. Higher educational institutions, human resources professionals, as well as experts that are already employed or aspire to be employed in Industry 4.0 organizations, would benefit from the results of our analysis. 相似文献