文章摘要
范丽鹏,王曰芬,李塽.大数据与创新双驱动的知识创新服务需求与趋势研究[J].情报工程,2019,5(3):022-032
大数据与创新双驱动的知识创新服务需求与趋势研究
Research on Demand and Trend of Knowledge Innovation Service Based on Big Data and Innovation
  
DOI:10.3772/j.issn.2095-915X.2019.03.003
中文关键词: 知识创新;知识创新服务;服务需求;趋势研究;大数据驱动;创新驱动发展
英文关键词: Knowledge innovation; knowledge innovation service; service demand; trend research; driven by big data; development driven by innovation
基金项目:本文系国家社会科学基金重大项目“ 面向知识创新服务的数据科学理论与方法研究”(16DZA224)和南京理工大学科研创新计划“ 面向知识创新服务的数据科学理论与方法体系构建研究” 研究成果之一。
作者单位
范丽鹏 南京理工大学经济管理学院 
王曰芬 1.南京理工大学经济管理学院 2.江苏省社会公共安全科技协同创新中心 
李塽 南京理工大学经济管理学院 
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中文摘要:
      大数据发展与科研创新的有机结合,共同驱动着知识创新服务新业态的形成。研究创新活动中的用户需求、服务供应商需要解决的问题与知识创新服务趋势及发展对策,是构建知识创新服务理论与方法体系的重要组成部分。通过问卷调研科研用户与实地访谈知识服务供应商两种形式,遵循科学研究流程,采用统计分析、比较综合、归纳提炼等方法开展研究。研究结果表明:创新用户目前较多使用传统方式获取信息,但对知识集成融合等知识加工类服务有了更高的需求;服务供应商多重视多源数据集成、多维度知识关联、个性化推荐服务等知识加工类服务面临的困境;未来的知识创新服务发展趋势是倾向于构建基于科研众包平台的学术交流共享服务、提供数据类服务、对现有服务进行多样化推广、向着专业化个性化方向发展、重视新技术在服务方面的应用等。
英文摘要:
      The combination of big data and scientific research innovation drives the formation of new forms of knowledge innovation services. Researching the user needs in innovation activities, problems that service providers need to solve, and trends and development strategies in knowledge innovation services are important components in building knowledge innovation service theory and method systems. Through questionnaire innovative users and interviewing service providers, this study follow the scientific research process and conduct research using statistical analysis, comparative synthesis, induction and refining. The results showed that innovative users tend to use traditional methods to obtain information, but there is a higher demand for knowledge processing services such as knowledge integration and integration. Moreover, service providers pay more attention to the difficulties of multi-source data integration, multi-dimensional knowledge association, personalized recommendation and other knowledge processing In the future, knowledge innovation services tend to create services based on crowdsourcing platforms, provide data services, promote existing services with diversified methods, develop professionalized and personalized recommendation services, and emphasis on the application of technology.
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