Knowledge based decision support system

Welcome to this special issue of the Information Technology and Management journal on knowledge based decision support systems. The main goal for this special issue is to be a timely vehicle for publishing selected research papers from academia and practitioners in different industries on this emerging topic. Knowledge-based decision support systems are systems designed to ensure more precise decision-making by effectively using timely and appropriate data, information, and knowledge management for convergence industry. These systems refer to decision-making based on relevant knowledge, which is based on artificial intelligence, and on the application of information and communication technologies. In addition, these systems support decisionmaking through prediction and recommendation techniques. Depending on the criteria, there are various classifications. Based on the knowledge used for deduction, data is classified into knowledge-based systems using dictionary-defined knowledge, and non-knowledge–based systems using machine learning and multi-dimensional statistical pattern recognition techniques [1–3]. This special issue covers some of the hottest topics in knowledge-based decision support systems, including: Decision Support for Convergence; Knowledge-based Applications and Management; Knowledge Acquisition and Representation; Knowledge Bases; Knowledge based Recommendation Systems; Data Modeling; Database Management Systems; Data Mining; Management Systems; Intelligent Healthcare Systems and Management; Decision Support Systems and Management; Machine Learning; Systems Analysis; and Design and Development. The paper by Lee et al. [4] presents a clinical decision support system in medical knowledge literature reviews. They propose a method to increase performance and efficiency in a clinical decision support system (CDSS), and enhance the understanding of the CDSS for better health management among physicians and patients. To add structure to the current study, major research areas were categorized based on a multidimensional unfolding analysis. The academic outlook of medical informatics could be forecasted, and academic quality could be improved by addressing the problems arising out of system development and realization processes. The paper by Lee [5] presents factors influencing a social networking service (SNS) user’s value perceptions, and word-of-mouth (WOM) decisions of corporate posts with special reference to emotional attachment. This study proposes a research model of the social knowledge value perception and WOM decision variable, including several precedent variables of a user’s personal value factors, such as emotional attachment, self-esteem, and self-exposure. The SNS user recognizes the value of social knowledge through emotional and personal factors. & Kyungyong Chung [email protected]