Special Issue on: "Knowledge Management Systems"
Prof. Tai-hoon Kim, Sungshin University, Korea
Prof. Carlos Ramos, Polytechnic of Porto, Portugal
Prof. Sabah Mohammed, Lakehead University, Canada
Prof. Adrian Stoica, NASA, USA
Knowledge management systems (KMS) are distinct from transaction processing systems, decision support systems or expert systems because of their main mission to transform experiences into explicit knowledge within an organization or given information processing place. Experience is an important and critical part of KMS because, when individuals receive new information, the information is processed in light of past experience to develop and create new knowledge.
The objective of this special issue is to present research exploring the creation of knowledge repositories, improving knowledge assets, enhancing the knowledge environment, managing knowledge as an asset, and big data and analytics.
This special issue will aid scientists, policy makers and professionals by discussing the latest theories, state-of-the-art techniques and applications. Special emphasis will be placed on the interaction between theoretical concepts and practical implementations, the exchange between policy analysts and policy makers, and the interface between analytic concepts and human and organisational problem solvers.
Suitable topics include, but are not limited to, the following:
- Techniques, methods and models in accessing big data as part of KMS
- Case studies of utilising big data in KMS initiatives
- How filtered big data results can be used continuously as organisational knowledge
- Big data and its implications for personal knowledge management
- Identifying gaps between traditional data and information approaches and the opportunities in big data
- Market analysis - systems vendors and organisational users
- New organisational big data and analytics initiatives as part of knowledge management strategy and practice
- Trends in big data - e-commerce
- M-commerce and ubiquitous commerce
- Internet of Things and big data for knowledge management
Notes for Prospective Authors
Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. (N.B. Conference papers may only be submitted if the paper has been completely re-written and if appropriate written permissions have been obtained from any copyright holders of the original paper).
All papers are refereed through a peer review process.
All papers must be submitted online. To submit a paper, please read our Submitting articles page.
If you have any queries concerning this special issue, please email Tai-hoon Kim at firstname.lastname@example.org.
Manuscripts due by: 30 November, 2017