共查询到20条相似文献，搜索用时 15 毫秒
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地球科学是以地球各圈层相互作用及其资源与环境效应为研究对象，多学科交叉融合的一门科学。地球科学既研究过去，阐述地球演化历史，又面向未来，为人类可持续发展提供解决方案，是一门既能拓展人类知识前沿又能服务于人类社会经济发展的综合性、系统性的科学。文章在梳理国际地球科学发展趋势和总结我国地球科学发展现状与科技资助情况的基础上，提出我国地球科学发展应从系统科学和国家需求的视角部署未来发展方向和研究计划。从地球系统整体出发，关注地球系统各圈层、各要素之间的相互作用及其对人类社会经济发展的影响。以“四个面向”引领地球科学领域研究的发展方向，开展有组织的重大科学研究，完善科技资助与评价机制，使研究成果更好地服务于国家重大战略需求。 相似文献
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《Information processing & management》2022,59(1):102736
Business intelligence (BI) incorporates business research, data mining, data visualization, data tools,infrastructure, and best practices to help businesses make more data-driven choices.Business intelligence's challenging characteristics include data breaches, difficulty in analyzing different data sources, and poor data quality is consideredessential factors. In this paper, IoT-based Efficient Data Visualization Framework (IoT- EDVF) has been proposed to strengthen leaks' risk, analyze multiple data sources, and data quality management for business intelligence in corporate finance.Corporate analytics management is introduced to enhance the data analysis system's risk, and the complexity of different sources can allow accessing Business Intelligence. Financial risk analysis is implemented to improve data quality management initiative helps use main metrics of success, which are essential to the individual needs and objectives. The statistical outcomes of the simulation analysis show the increasedperformance with a lower delay response of 5ms and improved revenue analysis with the improvement of 29.42% over existing models proving the proposed framework's reliability. 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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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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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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Shared databases are used as tools for knowledge management. But contributing knowledge to the database is a public-goods dilemma. Its specific features are multidimensionality of the goods, resource overlap, interdependency of group members and the lack of a marginal unit of return for individual contributions. An analytical model is presented describing the individual- and group-level payoff function. It shows that rewarding contributions with a cost-compensating bonus can be an effective solution not only at the individual level, but also at the group level. Whether or not a bonus system is self-supporting depends on the critical mass of people on average needing a contribution. This depends on the overlap of the people's resources and their interdependency. 相似文献
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【目的/意义】分析智慧图书馆知识发现服务驱动模式,促进图书馆数据资产开发及智慧服务提升。【目的/
意义】基于数据驱动,围绕用户、内容、分析、服务、环境五大数据驱动元素,分析其数据驱动过程与维度,通过驱动
维度间的作用关系与相互关联建立动力机制模型。【结果/结论】从动力机制运行角度来说,数据驱动就是数据流
转,不同数据驱动维度间的作用力关系均为正向,通过数据输入→语义关联→可视化分析→反馈调整的数据流转,
实现与图书馆基础数据库、机构知识库、专家数据库、学科馆员库等数据库之间数据协作,并在数据收集方式、数据
挖掘技术、数据评价模式、软硬件条件方面追求数据智能化。【创新/局限】数据属性化维度关系着数据收集的实现,
价值转化力与主体智慧度维度决定着数据到知识的转化与提升,数据物质性维度是实现驱动直接绩效的来源,并
由此反馈关联到驱动的出发点与落脚点。 相似文献
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《International Journal of Information Management》2016,36(3):451-475
ContextNowadays the concept of knowledge mapping has attracted increased attention from scientists in a variety of academic disciplines and professional practice areas. Among the most important attributes of a knowledge map is its ability to increase communication and share common practices across an entire organisation. However, despite being a promising area for research, the knowledge maps community lacks a widespread understanding of the current state of the art.ObjectiveThe objective of this article is to explore the world of knowledge mapping by reviewing and analysing the current state of research and providing an overview of knowledge mapping’s concepts, benefits, techniques, classifications and methodologies, which are precisely reviewed, and their features are highlighted. In addition, we offer directions for future research.MethodBased on the systematic literature review method this study collects, synthesises, and analyses numerous articles on a variety of topics closely related to a knowledge map published from January 2000 to December 2013 on six electronic databases by following a pre-defined review protocol. The articles have been retrieved through a combination of automatic and manual search, hence extensive quantitative and qualitative results of the research are provided.ResultsFrom the review study, we identified 132 articles addressing knowledge maps that have been reviewed in order to extract relevant information on a set of research questions. We found a generally increasing level of activity during this 5-year period. We noted that while existing research covers a large number of studies on some disciplines, such as systems and tools development, it contains very few studies on other disciplines, such as knowledge maps adoption. To aid this situation, we offer directions for future research.ConclusionsThe results demonstrated that a knowledge map is an imperative strategy for increasing organisations’ effectiveness. In addition, there is a need for more knowledge maps research. 相似文献
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[目的/意义]研究智能情报分析模式特点、发展方向及研究建设策略。[方法/过程]在分析神经网络、知识工程两类人工智能核心技术演变过程及发展趋势的基础上,提出支撑智能情报分析服务的大数据资源体系框架结构及建设原则,并分析数据驱动和知识驱动方法在情报分析中的单独应用与融合发展问题。[结果/结论]认为数据和知识双轮驱动是智能情报分析未来发展的一个方向;而实现智能情报分析的关键,一是加强一次数据资源和二次数据资源建设,二是加强深度学习技术与知识图谱技术在科技情报分析中应用的研究,三是建立数据驱动与知识驱动相融合的分析模式。 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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《Information processing & management》2019,56(6):102093
Knowledge graphs are widely used in retrieval systems, question answering systems (QA), hypothesis generation systems, etc. Representation learning provides a way to mine knowledge graphs to detect missing relations; and translation-based embedding models are a popular form of representation model. Shortcomings of translation-based models however, limits their practicability as knowledge completion algorithms. The proposed model helps to address some of these shortcomings.The similarity between graph structural features of two entities was found to be correlated to the relations of those entities. This correlation can help to solve the problem caused by unbalanced relations and reciprocal relations. We used Node2vec, a graph embedding algorithm, to represent information related to an entity's graph structure, and we introduce a cascade model to incorporate graph embedding with knowledge embedding into a unified framework. The cascade model first refines feature representation in the first two stages (Local Optimization Stage), and then uses backward propagation to optimize parameters of all the stages (Global Optimization Stage). This helps to enhance the knowledge representation of existing translation-based algorithms by taking into account both semantic features and graph features and fusing them to extract more useful information. Besides, different cascade structures are designed to find the optimal solution to the problem of knowledge inference and retrieval.The proposed model was verified using three mainstream knowledge graphs: WIN18, FB15K and BioChem. Experimental results were validated using the hit@10 rate entity prediction task. The proposed model performed better than TransE, giving an average improvement of 2.7% on WN18, 2.3% on FB15k and 28% on BioChem. Improvements were particularly marked where there were problems with unbalanced relations and reciprocal relations. Furthermore, the stepwise-cascade structure is proved to be more effective and significantly outperforms other baselines. 相似文献
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科学技术是一个国家综合国力的重要体现,而基础科学则是科学技术发展的基础。只有重视基础研究,才能不断增强自主创新能力。基础研究作为知识和技术的储备,既保证为社会经济发展持续供给科技成果,又能拓展人类知识前沿并促进社会进步。当前,世界各国重视基础科学研究对于经济社会发展的实质性作用,科技创新政策和治理日益"以任务为导向"。未来基础科学发展更要关注基础科学对于社会经济发展的实际作用。文章从社会需求出发,以人为关键要素,重点阐述生命健康、社会发展与自然环境改善、变革性技术发展和提升基础科学能力的方法等4个方面的基础科学研究进展。 相似文献
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Changjun Lee Dieter Franz Kogler Daeho Lee 《Information processing & management》2019,56(4):1576-1591
In addressing persistent gaps in existing theories, recent advances in data-driven research approaches offer novel perspectives and exciting insights across a spectrum of scientific fields concerned with technological change and the socio-economic impact thereof. The present investigation suggests a novel approach to identify and analyze the evolution of technology sectors, in this case, information and communications technology (ICT), considering international collaboration patterns and knowledge flows and spillovers via information inputs derived from patent documents.The objective is to utilize and explore information regarding inventors’ geo-location, technology sector classifications, and patent citation records to construct various types of networks. This, in turn, will open up avenues to discover the nature of evolutionary pathways in ICT trajectories and will also provide evidence of how the overall ICT knowledge space, as well as directional knowledge flows within the ICT space, have evolved differently. It is expected that this data-driven inquiry will deliver intuitive results for decision makers seeking evidence for future resource allocation and who are interested in identifying well-suited collaborators for the development of potential next-generation technologies. Further, it will equip researchers in technology management, economic geography, or similar fields with a systematic approach to analyze evolutionary pathways of technological advancements and further enable exploitation and development of new theories regarding technological change and its socio-economic consequences. 相似文献
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【目的/意义】从大数据驱动角度出发,探索采用人工智能方法实现对政策文本协同性定量分析的可能性。
【方法/过程】以政策全文本数据为研究对象,使用知识图谱技术实现不同主题的本体构建,并应用数据挖掘中关联
规则构建推理模型,对图谱表示的政策文本进行协同性语义挖掘和推理。【结果/结论】围绕“开放数据”和“数据安
全”主题构建知识图谱,实现对政策文本的本体表示,在此基础上使用关联规则完成单文本和多文本在两个主题间
的协同性分析。【创新/局限】本文将知识图谱应用于政策文本分析领域,并完成协同性分析,为政策的全样本分析
提供可能性,后续需扩大样本规模,提升推理效率。 相似文献
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The challenges of managing large complex engineering projects, such as those involving the design of infrastructure, aerospace and industrial systems; are widely acknowledged. While there exists a mature set of project management tools and methods, many of today's projects overrun in terms of both time and cost. Existing literature attributes these overruns to factors such as: unforeseen dependencies, a lack of understanding, late changes, poor communication, limited resource availability (inc. personnel), incomplete data and aspects of culture and planning. Fundamental to overcoming these factors belies the challenge of how management information relating to them can be provided, and done so in a cost eff ;ective manner. Motivated by this challenge, recent research has demonstrated how management information can be automatically generated from the evolving digital footprint of an engineering project, which encompasses a broad range of data types and sources. In contrast to existing work that reports the generation, verification and application of methods for generating management information, this paper reviews all the reported methods to appraise the scope of management information that can be automatically generated from the digital footprint. In so doing, the paper presents a reference model for the generation of managerial information from the digital footprint, an appraisal of 27 methods, and a critical reflection of the scope and generalisability of data-driven project management methods. Key findings from the appraisal include the role of email in providing insights into potential issues, the role of computer models in automatically eliciting process and product dependencies, and the role of project documentation in assessing project norms. The critical reflection also raises issues such as privacy, highlights the enabling technologies, and presents opportunities for new Business Intelligence tools that are based on real-time monitoring and analysis of digital footprints. 相似文献
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《International Journal of Information Management》2017,37(6):726-734
The activities in our current world are mainly supported by data-driven web applications, making extensive use of databases and data services. Such phenomenon led to the rise of Data Scientists as professionals of major relevance, which extract value from data and create state-of-the-art data artifacts that generate even more increased value. During the last years, the term Data Scientist attracted significant attention. Consequently, it is relevant to understand its origin, knowledge base and skills set, in order to adequately describe its profile and distinguish it from others like Business Analyst. This work proposes a conceptual model for the professional profile of a Data Scientist and evaluates the representativeness of this profile in two commonly recognized competences/skills frameworks in the field of Information and Communications Technology (ICT), namely in the European e-Competence (e-CF) framework and the Skills Framework for the Information Age (SFIA). The results indicate that a significant part of the knowledge base and skills set of Data Scientists are related with ICT competences/skills, including programming, machine learning and databases. The Data Scientist professional profile has an adequate representativeness in these two frameworks, but it is mainly seen as a multi-disciplinary profile, combining contributes from different areas, such as computer science, statistics and mathematics. 相似文献