Data Analysis and Knowledge Discovery
Data Analysis and Knowledge Discovery is a scholarly research journal founded in 2017, published monthly by the National Science Library of Chinese Academy of Sciences, under the auspices of Chinese Academy of Sciences.
The Journal focuses on basic and applied research of theories, methods, systems, and best practices, for big data-based and computationally analytics-driven decision &policy analysis, in all the data-intensive and knowledge-driven fields. Special attention is given to computational discovery to detect and predict structures, trends, behaviors, relations, disruptions, and evolutions.
The journal takes full advantages of the convergence of computer science, complexity theories, data science, management science, policy research, behavior science, scientometrics, social metrics, digital science & digital humanities, and information science. The journal aims to support the research & application to transform data to information to knowledge to wisdom to intelligent solutions, and to embed the theories, technologies, and practices into intelligent management and decision-making in all the fields and industries.
The journal adopts an evolving definition of data, including metadata or full content data, text or non-textural data, structured or non-structural data, domain specific or cross-domain data, rich media or compound data, and interactive data. The journal builds upon the development of theories, methods, technologies, and practices from many fields to stimulate, support, and strengthen data-based, semantically rich, computationally sound, and analytic-driven knowledge discovery theories, methods, tools, supporting infrastructures, and enabling policies.
The main themes include:
(1) New theories, methods and techniques based on big data mining and knowledge discovery, which include, but not limited to, feature or entity learning, content understanding, knowledge graphs, knowledge mapping, complex network analysis, scientometrics, social metrics, business intelligence, social intelligence, intelligent decision computing, policy analytics and intelligent development.
(2) Innovative knowledge infrastructural tools or systems, based on data analysis and knowledge organization, that inherently enable and support knowledge discovery and intelligent decision making. These may manifest as intelligent GLAM (Gallery, Library, Archival, and Museum), intelligent labs in digital science, intelligence campus, intelligent transportation, intelligent cities, and many other emerging services and platforms. This may include but not limited to linked open data, intelligent classification and annotation, ontology development, knowledge mapping and fusion, workflow and context development, and semantically interactive IaaS and PaaS, and domain specific knowledge infrastructure.
(3) New applied methods, techniques and systems, based on data analysis and knowledge discovery, to improve and upgrade various forward tracking, strategic planning, creativity and innovation stimulation, operational management, evaluation, and ROI analysis, etc. in fields such as scholarly communication, media services, research management, learning management, innovation and entrepreneurship management, cooperation management, emergency management, and institutional planning and management, among others.