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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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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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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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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
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Gustavo Dalmarco Filipa R. Ramalho Ana C. Barros Antonio L. Soares 《The Journal of High Technology Management Research》2019,30(2):100355
The concept of industry 4.0 (i4.0) encompasses the integration of different technologies into an autonomous, knowledge- and sensor-based, self-regulating production system. Our objective is to synthesize which are the challenges and opportunities of adopting i4.0 from the perspective of technology provider companies. A single-case research was conducted with ten companies at the Portuguese Production Technologies Cluster. Based on i4.0 technologies – Augmented reality; Additive Manufacturing; Big Data; Cloud Computing; Cyber-Physical Systems; Cybersecurity; Smart Robotics; Simulation; and System Integration – interviewees mentioned that the main adoption challenges are the analysis of data generated, integration of new technologies with available equipment and workforce, and computational limitations. The main opportunities are improvements in: efficiency; flexibility; productivity; cybersecurity; quality of products and services; and decision process due to data analysis. Interviewees have also foreseen changes in company's business model through the integration of internal resources with complementary activities of their partners and other cluster companies. 相似文献
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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
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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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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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《International Journal of Information Management》2017,37(3):150-154
Open data aims to unlock the innovation potential of businesses, governments, and entrepreneurs, yet it also harbours significant challenges for its effective use. While numerous innovation successes exist that are based on the open data paradigm, there is uncertainty over the data quality of such datasets. This data quality uncertainty is a threat to the value that can be generated from such data. Data quality has been studied extensively over many decades and many approaches to data quality management have been proposed. However, these approaches are typically based on datasets internal to organizations, with known metadata, and domain knowledge of the data semantics. Open data, on the other hand, are often unfamiliar to the user and may lack metadata. The aim of this research note is to outline the challenges in dealing with data quality of open datasets, and to set an agenda for future research to address this risk to deriving value from open data investments. 相似文献
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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
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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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《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
16.
郭华东 陈润生 徐志伟 孙建军 毕军 王力哲 骆健俊 沈华伟 顾东晓 梁栋 沈文庆 张旭 Hans Wolfgang Spiess Thomas Lengauer 《中国科学院院刊》2016,31(6):707-716
大数据是知识经济时代的战略高地,是国家和全球的新型战略资源。作为思维的革命性创新,大数据为科学研究带来了新的方法论。第六届中德前沿探索圆桌会议以"自然科学与人文科学大数据"为主题,在"生物医药大数据"、"物理、化学与地球科学领域大数据"、"人文与社会科学领域大数据"和"大数据处理技术与方法"4个领域进行研讨,总结了大数据对于科学发现的重要作用、意义以及面临的重大问题,形成了关于发展科学大数据研究的相关建议。 相似文献
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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
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《Information processing & management》2022,59(1):102758
Advancements in recent networking and information technology have always been a natural phenomenon. The exponential amount of data generated by the people in their day-to-day lives results in the rise of Big Data Analytics (BDA). Cognitive computing is an Artificial Intelligence (AI) based system that can reduce the issues faced during BDA. On the other hand, Sentiment Analysis (SA) is employed to understand such linguistic based tweets, feature extraction, compute subjectivity and sentimental texts placed in these tweets. The application of SA on big data finds it useful for businesses to take commercial benefits insight from text-oriented content. In this view, this paper presents new cognitive computing with the big data analysis tool for SA. The proposed model involves various process such as pre-processing, feature extraction, feature selection and classification. For handling big data, Hadoop Map Reduce tool is used. The proposed model initially undergoes pre-processing to remove the unwanted words. Then, Term Frequency-Inverse Document Frequency (TF-IDF) is utilized as a feature extraction technique to extract the set of feature vectors. Besides, a Binary Brain Storm Optimization (BBSO) algorithm is being used for the Feature Selection (FS) process and thereby achieving improved classification performance. Moreover, Fuzzy Cognitive Maps (FCMs) are used as a classifier to classify the incidence of positive or negative sentiments. A comprehensive experimental results analysis ensures the better performance of the presented BBSO-FCM model on the benchmark dataset. The obtained experimental values highlights the improved classification performance of the proposed BBSO-FCM model in terms of different measures. 相似文献
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近年来,大数据技术与系统在性能和效率方面已经取得了显著的提升,大数据应用到各个行业,赋能产业智能化发展,成为信息社会进入智能化阶段的关键要素。然而,大数据技术发展也面临着更深层次的挑战,如数据泛滥与高价值数据缺失并存、大数据分析研判复杂不确定、数据流通共享与数据可信安全使用难以兼顾等。这些挑战将推动大数据分析处理技术的创新变革,促进新技术体系的建立与发展。文章面向大数据分析处理面临的新架构、新模式、新范式和安全可信需求,提出构建新一代大数据分析处理系统栈,探索大数据价值利用新范式,并展望新技术体系下的牵引性需求与重大应用。 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