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2022

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【论文速递】从商业智能到科研智能:智能化时代的科学学与科技管理


摘要: 商业智能作为企业管理中一种数据驱动的智能化管理工具,可以显著提高企业的决策水平和管理能力。作为企业管理的兄弟领域,科技管理无论在信息化方面还是在智能化方面,与前者相比都有着不小的差距。在大数据和人工智能技术蓬勃发展的今天,科学学与科技管理能不能借鉴商业智能的工具和商科思维方式,更好的发挥数据在科技管理中的作用,决定科技管理未来能不能更好的服务于我国科技事业的发展需要。文章提出并梳理了科研智能的概念,总结了科研智能的理论渊源和发展路径,介绍了科研智能的整体框架和关键技术,并阐述了科研智能的学术价值和现实意义。

 

关键词:科研智能;商业智能;科学学与科技管理;科研信息化;科学计量学

 

Abstract: Human society is entering an era which is filled of informationization, big data and intelligence. New concepts such as artificial intelligence, data intelligence, organizational intelligence, and business intelligence are emerging in endlessly. Among them, business intelligence, as the most mature, specific and widely used concept has undergone several connotation evolutions since it been put forward in the late 1980 s, which has become an important data analysis and display system for enterprise decision support, and can solve the problem of data support in decision-making and operation for enterprises, greatly improving the level and efficiency of enterprise management. So, in science management, can we use business intelligence thinking and tools to provide decision-making assistance and management support for science management? In this context, we put forward a concept of "science intelligence". The theoretical origin of the concept of science intelligence mainly includes the following two points. Firstly, science intelligence is the application of business intelligence technology in scientific research. It borrows mature technologies and solutions to solve the specific problems in science managementin the field of business intelligence. Secondly, science intelligence is needed by the times for the development of scientific informatization and scientometrics. At present, we already have a relatively complete scientific research data infrastructure. The key issue that needs to be focused on in the following part is how to upgrade and transform at the software level and integrate scattered scientific research data.Based on the thoughts and technologies of business intelligence, the research designed the overall framework of science intelligence. It integrates modern data warehousing technology, data mining technology and data dashboard technology, and integrates semantic information processing, knowledge search and recommendation, chart dashboard, mapping knowledge domains and other modules. In this framework, the bottom layer is a multi-source heterogeneous science database, which is built through scientific research big data analysis, entity relationship diagram construction, index system design, etc.; the middle is science intelligence concepts, theories, methods, and the toolset root in business intelligence; the upper layer is the application layer, which not only includes the specific functions of science intelligence such as data filtering, data drill-down, and association charts, but also specific functions in science monitoring, evaluation, and diagnosis. Among them, the key technologies in the opening of science intelligence systems is how to build a multi-source heterogeneous database for semi-structured and unstructured data and how to realize the transformation from a static non-interactive knowledge graph to a dynamic interactive visualization graph, and how to establish science intelligence modules and processes for science and technology policy and science management. Science intelligence has important theoretical and practical significance for future-oriented science of science and S.&T. management. Since the reform and opening up, especially the 18 th National Congress of the Communist Party of China, China has made great achievements in science and technology and has achieved remarkable results. However, there are still some shortcomings which restrict the further improvement of technological innovation capabilities and global competitiveness in the development of science and technology of China. One of the important ways to solve the problem is to rely on the science intelligence technology to improve the modern informatization level of science and technology management. At the same time, at the micro level, with the help of science intelligence, only continuously improve the level of intelligence in the management of science and technology in universities or research institutes, can we provide better services for scientists. Business administration and technology management are the sibling fields in management disciplines. Under the environment of market economy, the research interest in business management is rising, while the research on science and technology management in universities and scientific research institutes is declining, and both academic research and specific applications are beginning to lag behind business management. Whether can use the successful experience of business management and learn from mature tools such as business intelligence to reshape the fields of science of science and S.&T. management will become a necessary path for the development of it.

 

Keyword:science intelligence; business intelligence; science and technology management; e-science; scientometrics

 

文章作者:胡志刚 王欣 李海波

作者单位:大连理工大学科学学与科技管理研究所

齐鲁工业大学(山东省科学院)

全文已刊发在《科学学与科学技术管理》2021年第1期

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