The Journal of Data and Information Science (JDIS), sponsored by the Chinese Academy of Sciences (CAS), is published quarterly by the National Science Library of CAS. Launched in 2016, JDIS is the first internationally published English-language academic journal in library and information Science and related fields from China.
JDIS devotes itself to the study and application of the theories, methods, techniques, services, and infrastructural facilities using big data to support knowledge discovery for decision and policy making. The basic emphasis is research that focuses on big data, analytics, knowledge-discovery, and supports decision making. Special attention is given to knowledge discovery to detect and predict structures, trends, behaviors, relations, evolutions, and disruptions in scientific research. This includes issues of innovation, business, politics, security, media and communications, and social development. The big data topics may include metadata or full content data, text or non-textural data, structured or non-structured data, domain specific or cross-domain data, and dynamic or interactive data. The main areas of interest are:
1) New theories, methods, and techniques of big data-based data mining, knowledge discovery, and informatics, including but not limited to communication analysis, social network analysis, tech and industry analysis, scientometrics, competitive intelligence, knowledge mapping, evidence-based policy analysis, scientometrics, and predictive analysis.
2) New methods, architectures, and facilities to develop or improve knowledge infrastructure that can support knowledge organization and sophisticated analytics, including but not limited to ontology construction, knowledge organization, semantic linked data, knowledge integration and fusion, semantic retrieval, domain-specific knowledge infrastructure, and semantic sciences.
3) New mechanisms, methods, and tools to embed knowledge analytics and knowledge discovery into actual operation, service, or managerial processes, including but not limited to knowledge-assisted scientific discovery, and data mining-driven intelligent workflows in learning, communications, or management.
Contact:
Editorial Office
National Science Library, Chinese Academy of Sciences
33 Beisihuan Xilu, Haidian, Beijing 100190, P.R. China
Tel: +86-10-62568693
Email: jdis@mail.las.ac.cn;
mengp@mail.las.ac.cn
Publisher
DE GRUYTER OPEN
Bogumi？a Zuga 32A Str.
01-811 Warsaw, Poland
T: +48 22 701 50 15
