An Architecture for Context-Aware Knowledge Flow Management Systems
The organizational knowledge is one of the most important and valuable assets of organizations. In such environment, organizations with broad, specialized and up-to-date knowledge, adequately using knowledge resources, will be more successful than their competitors. For effective use of knowledge, dynamic knowledge flow from the sources to destinations is essential. In this regard, a novel complex concept in knowledge management is the analysis, design and implementation of knowledge flow management systems. One of the major challenges in such systems is to explore the knowledge flow from the source to the recipient and control the flow for quality improvements concerning the users needs as possible. Therefore, the purpose of this paper is to provide an architecture in order to solve this challenge. For this purpose, in addition to the architecture for knowledge flow management systems, a new node selection strategy is provided with higher success rate compared to previous strategies.
Keywords: Knowledge Management, knowledge flow management system, knowledge sharing, knowledge quality improvement.
Download Full-Text
ABOUT THE AUTHORS
Ali Jarrahi
Ali Jarrahi received his B.S. and M.S. degrees in computer engineering in 2008 and 2011, respectively. His research interests include knowledge management systems, data mining, and Algorithms & Theory.
Mohammad Reza Kangavari
Dr. Mohammad Reza Kangavari is Assistant Professor within Department of Computer Engineering (CE), Iran University of Science and Technology. His research interests include Artificial Intelligence, Data Mining, Knowledge Management, and Algorithms & Theory.
Ali Jarrahi
Ali Jarrahi received his B.S. and M.S. degrees in computer engineering in 2008 and 2011, respectively. His research interests include knowledge management systems, data mining, and Algorithms & Theory.
Mohammad Reza Kangavari
Dr. Mohammad Reza Kangavari is Assistant Professor within Department of Computer Engineering (CE), Iran University of Science and Technology. His research interests include Artificial Intelligence, Data Mining, Knowledge Management, and Algorithms & Theory.
