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1.
【目的/意义】人工智能的融入将推动图书馆在公共服务领域的业态革新,提升图书馆公共文化服务效能,
促进智慧图书馆建设过程中各领域的服务创新。【方法/过程】本文以SCI、SSCI、Scopus数据库为来源,通过对文献
进行内容分析和主题挖掘,呈现智慧图书馆研究的热点主题内容和热点应用场景、AI在智慧图书馆中的未来研究
趋势以及AI为智慧图书馆建设带来的机遇和挑战。【结果/结论】从热点主题内容上看,本文梳理了学者们的主要研
究兴趣;根据AI的技术优势以及智慧图书馆的业务需求,梳理出AI在智慧图书馆中的五大应用场景,并提出该领
域未来四个研究趋势;最后,结合AI技术未来最新的发展趋势,分析了AI为智慧图书馆带来的发展机遇,并提出AI
技术落地所面临的挑战。【创新/局限】本文分析了AI在智慧图书馆建设中的落地场景、未来研究趋势以及图书馆可
能面临的机遇与挑战;但研究的样本文献主要基于国外主流数据集,在未来研究中将进一步扩大样本文献。 相似文献
2.
【目的/意义】医疗健康大数据为智慧医疗提供了前所未有的机遇。然而“数据烟囱”“信息孤岛”和低效的
知识服务方法严重阻碍医疗健康服务模式创新。如何通过医疗健康大数据深度聚合和动态知识服务,实现面向全
方位全周期智慧医疗服务的知识管理创新成为当前医疗信息资源管理领域的重要问题。【方法/过程】介绍了一种
面向大规模多源异构医疗健康数据安全共享的联邦学习机制和深度聚合方法,提出了人机协同的医疗案例库构建
方法和基于杰卡德距离算法的医疗案例知识推理方法。【结果/结论】该方法为智慧诊疗、临床教学和辅助科研提供
了一体化知识管理服务框架。【创新/局限】该方法不仅为智慧医疗与精准健康管理提供了一种数据管理方法体系,
还为5P智慧医疗服务新模式构建提供了新的思路。 相似文献
3.
Clinicians, healthcare providers-suppliers, policy makers and patients are experiencing exciting opportunities in light of new information deriving from the analysis of big data sets, a capability that has emerged in the last decades. Due to the rapid increase of publications in the healthcare industry, we have conducted a structured review regarding healthcare big data analytics. With reference to the resource-based view theory we focus on how big data resources are utilised to create organization values/capabilities, and through content analysis of the selected publications we discuss: the classification of big data types related to healthcare, the associate analysis techniques, the created value for stakeholders, the platforms and tools for handling big health data and future aspects in the field. We present a number of pragmatic examples to show how the advances in healthcare were made possible. We believe that the findings of this review are stimulating and provide valuable information to practitioners, policy makers and researchers while presenting them with certain paths for future research. 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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《International Journal of Information Management》2016,36(5):748-758
The expansion of big data and the evolution of Internet of Things (IoT) technologies have played an important role in the feasibility of smart city initiatives. Big data offer the potential for cities to obtain valuable insights from a large amount of data collected through various sources, and the IoT allows the integration of sensors, radio-frequency identification, and Bluetooth in the real-world environment using highly networked services. The combination of the IoT and big data is an unexplored research area that has brought new and interesting challenges for achieving the goal of future smart cities. These new challenges focus primarily on problems related to business and technology that enable cities to actualize the vision, principles, and requirements of the applications of smart cities by realizing the main smart environment characteristics. In this paper, we describe the state-of-the-art communication technologies and smart-based applications used within the context of smart cities. The visions of big data analytics to support smart cities are discussed by focusing on how big data can fundamentally change urban populations at different levels. Moreover, a future business model of big data for smart cities is proposed, and the business and technological research challenges are identified. This study can serve as a benchmark for researchers and industries for the future progress and development of smart cities in the context of big data. 相似文献
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The computerized healthcare information system has undergone tremendous advancements in the previous two decades. Medical institutions are paying further attention to the replacement of traditional approaches that can no longer handle the increasing amount of patient data. In recent years, the healthcare information system based on big data has been growing rapidly and is being adapted to medical information to derive important health trends and support timely preventive care. This research aims to evaluate organization-driven barriers in implementing a healthcare information system based on big data. It adopts the analytic network process approach to determine the aspect weight and applies VlseKriterijumska Optimizacija I Kzompromisno Resenje (VIKOR) to conclude a highly appropriate strategy for overcoming such barriers. The proposed model can provide hospital managers with forecasts and implications that facilitate the withdrawal of organizational barriers when adopting the healthcare information system based on big data into their healthcare service system. Results can provide benefits for increasing the effectiveness and quality of the healthcare information system based on big data in the healthcare industry. Therefore, by understanding the sequence of the importance of resistance factors, managers can formulate efficient strategies to solve problems with appropriate priorities. 相似文献
7.
《Information processing & management》2020,57(6):102358
This paper will describe Transformative Computing technologies as a new area of modern information sciences, which plays crucial role in development future IT technologies. Transformative computing allow to join communication technologies, and data processing techniques with advanced AI solutions, which allow to analytically process and manage acquired data. It enhances possibilities of efficient data analysis, and thanks to the application of AI, opens a new areas of data exploration towards planning, decision supporting, and advanced secure information management in distributed systems. In this paper we'll focus on new possible areas of transformative computing applications, especially for semantic information processing, as well as cognitive data reasoning. 相似文献
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【目的/意义】跨学科是当今科学发展的显著特征,以跨学科研究特征为主的跨学科性研究探索,不仅为厘
清跨学科研究主题提供重要线索,而且为跨学科研究管理和评价提供依据。【方法/过程】以跨学科性研究为研究对
象,借助Coherence Score与LDA相结合主题挖掘模型识别跨学科性研究的主要主题,并通过文献计量法从宏观和
微观层次探索跨学科性研究演化特征。【结果/结论】结果显示,对跨学科研究的跨学科性评估正处于快速发展期,
跨学科研究不仅来源于社会科学、生态学等学科跨界探索的内在驱动,也源于气候变化、生态环境脆弱性等复杂问
题的外在驱动;跨学科研究评价指标与方法复杂综合,融合大数据、人工智能等新技术是发展趋势;多种形式的跨
学科教育与科研合作是促进跨学科研究落实的根本。【创新/局限】多维剖析跨学科性研究热点与未来发展趋势,为
国家科技政策制定和科学研究者开展相关研究提供参考。 相似文献
9.
《Information processing & management》2022,59(2):102888
Human Intelligence is considered superior compared to Artificial Intelligence (AI) because of its ability to adapt faster to changes. Due to increasing data deluge, it is cumbersome for humans to analyse the vast amount of data and hence AI systems are in demand in today's world. However, these AI systems lack self-awareness, social skills, multitasking and faster adaptability. Cognitive Computing (CC), a subset of AI, acts as an effective solution in solving these challenges by serving as an important driver for knowledge-rich automation work. Knowing the latest research and state of the art in CC is one of the initial steps needed for researchers to make progress in this front. Thus, this paper presents a comprehensive survey of prior research in the CC domain along with the challenges, solutions and future research directions. Specifically, CC-based techniques solving real-world problems in four widely-researched application areas, namely, healthcare, cybersecurity, big data and IoT, have been reviewed in detail and the open research issues are discussed. 相似文献
10.
Pervasive digitization and aggregation of personal health information (PHI), along with artificial intelligence (AI) and other advanced analytical techniques, hold promise of improved health and healthcare services. These advances also pose significant data governance challenges for ensuring value for individual, organizational, and societal stakeholders as well as individual privacy and autonomy. Through a case study of a controversial public-private partnership between Royal Free Trust, a National Health Service hospital system in the United Kingdom, and Alphabet’s AI venture DeepMind Health, we investigate how forms of data governance were adapted, as PHI data flowed into new use contexts, to address concerns of contextual integrity, which is violated when personal information collected in one use context moves to another use context with different norms of appropriateness. 相似文献
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As part of the urgent need to respond to the COVID-19 pandemic, governments, healthcare providers, and businesses have looked to applications of Artificial Intelligence (AI) to compensate for the unavailability of human workers. This interest has renewed the debate regarding the use of AI for the automation of work, which has been described as Intelligent Automation (IA). A new dimension to this debate is whether COVID-19 will be the catalyst for higher IA adoption levels. This article reviews arguments in favour of COVID-19 increasing the level of IA adoption and possible counter-arguments. Key arguments in favour of increased IA adoption include consumer preferences changing to favour IA, increasing familiarity of IA technologies, and increased business confidence in IA. Counter-arguments include big data availability and reliability limitations, many tasks still favouring human skills over IA, the narrow capabilities of IA technologies, and a high availability of human workers. The article also discusses the implications of this debate for information management research and practice. 相似文献
13.
智慧农业伴随着人工智能、云计算、物联网、大数据等现代信息技术的发展应运而生。近年来,智慧农业在全球范围内逐渐被重视,在未来农业发展领域将扮演着极其重要的角色。本文主要通过探讨如何通过在智慧农业教学中普及人工智能与云计算方面的相关知识应用,从而推动智慧农业教学的进步与改革,让学生从根本上了解我国智慧农业发展至今其中所存在的问题和相应的解决方法,目的在于能够更快地在我国智慧农业起步阶段,提高农学学子的知识水平,在技术、应用、产业和机制方面突破瓶颈,来加速中国智慧农业的跨越式发展。 相似文献
14.
近几年,图书情报领域在新技术应用研究方面可谓百花齐放,网格技术、数据挖掘技术、射频识别技术、RSS技术、存储技术、Web2.0、智能手机技术、聚类技术等技术的研究不断呈上升趋势。文章选择2006年至2012年图书情报领域核心期刊的研究论文进行统计与主题分析,发现不同技术主题的研究脉络,找出研究中存在的问题,以期对未来的研究工作提供一定的理论基础。 相似文献
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[目的]为了实现海量数据中信息的知识组织,促进单元信息和文献信息内容的知识关联和知识发现,构建单元信息知识组织体系;[方法]以养生领域内的领域本体、文献信息等知识资源为基础,构建养生单元信息知识服务系统;[结果/结论]"养生单元信息知识服务系统"（以下简称"养生知识服务平台"）是单元信息知识组织体系建设的重要应用示范,提供语义检索、知识浏览、知识推理和知识发现等服务,实现大数据环境下"单元信息知识组织体系"的有效利用;[局限]文献资源的单元信息抽取及分析涉及人工智能、计算机处理等相关技术,技术实现有较大难度。 相似文献
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本文通过对新冠肺炎疫情应急管理中数字技术的实践应用案例研究发现,数字化治理可以在疫情关联信息获取、疫情传播路径追踪、疫情信息公开与舆情管理、疫情风险预警、健康认证等方面提供强力支撑,服务各地区进行精准防疫和决策支持。但同时也暴露出来若干问题,应对这些问题建议政府在推动应急管理体系的数字化治理实践中加快顶层设计;将城市公共管理的数字化转型融入智慧城市建设和城市大脑建设中;推动公共大数据的整合共享;加强数据、信息、网络与系统安全建设的同步规划和同步运行。 相似文献
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[目的/意义]为提高新兴技术主题识别的全面性、准确性,在专利、论文信息的基础上,融入舆情信息,提出一种复合型的新兴技术主题识别方法,以期能为微观层面新兴技术主题识别方法的研究提供参考。[方法/过程]面向专利、期刊论文、学位论文、会议论文、舆情共5种不同的科技文献类型,运用LDA模型从多源异构文本中获取主题词,并进行融合分析,提取候选新兴技术主题;设计识别指标体系,弥补现有指标体系缺乏未来前景相应指标的不足;运用CRITIC法进行综合评价,确定最终的新兴技术主题。[结果/结论]以智能网联汽车为实验对象,成功识别出多传感器融合信息技术这项新兴技术,实验结果与业界分析保持一致,说明多源信息视角下的多指标新兴技术主题识别方法能有效地对多源异构文本开展新兴技术的主题识别。 相似文献
18.
《Information processing & management》2020,57(5):102276
As handling fashion big data with Artificial Intelligence (AI) has become exciting challenges for computer scientists, fashion studies have received increasing attention in computer vision, machine learning and multimedia communities in the past few years. In this paper, introduce the progress in fashion research and provide a taxonomy of these fashion studies that include low-level fashion recognition, middle-level fashion understanding and high-level fashion applications. Finally, we discuss the challenges that when the fashion industry faces AI technologies. 相似文献
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The rapid development of information communication technology has led towards the emergence of the “connected world” characterised by the pervasive embeddedness of smart technologies. Smart technologies have a transformative impact on different domains of life. The application of smart technologies redefines the way people live, interact and conduct business. To date, the attention of the scholarly community has been paid primarily to smart cities, smart manufacturing and smart homes. However, despite numerous studies discussing the benefits of advanced technologies in the workplace, there is a lack of research on smart offices and how they affect productivity and employee well-being. This opinion paper argues that office spaces constitute a distinctive type of space, and research on smart homes or manufacturing does not suffice to capture its essence. Therefore, the aim of this paper is to propose a research agenda that can advance the current literature on smart and information communication technologies in relation to workplace spaces and the potential implications these could have on productivity. 相似文献