Knowledge Acquisition and Management

(KAM)

Belgrade, Serbia, 8–11 September, 2024

Knowledge management is a large multidisciplinary field having its roots in Management and Artificial Intelligence. Activity of an extended organization should be supported by an organized and optimized flow of knowledge to effectively help all participants in their work. 

We have the pleasure to invite you to contribute to and to participate in this Thematic Track. It builds on experiences of past events that have been organized for the first time in 1992, as a venue for scientists and practitioners to address different aspects of usage of advanced information technologies in management, with focus on intelligent techniques and knowledge management. In 2003 the focus of the event changed somewhat and it was organized for the first under its current name. Furthermore, it became an international event, with participants from around the world. In 2012 it was merged into FedCSIS becoming a foundation of one of its oldest Thematic Tracks.

The aim of this Thematic Track is to create possibility of presenting and discussing approaches, techniques and tools in the knowledge acquisition and other knowledge management areas with focus on contribution of artificial intelligence for improvement of human-machine intelligence and face the challenges of this century. We expect that the Thematic Track will enable exchange of information and experiences, and delve into current trends of methodological, technological and implementation aspects of knowledge management processes.

Topics

The following group topics, concerning both theory and applications, will be included (unavoidably incomplete):

  • Knowledge discovery from databases and data  warehouses
  • Methods and tools for knowledge acquisition
  • New emerging technologies for management
  • Organizing the knowledge centers  and knowledge distribution
  • Knowledge creation and validation
  • Knowledge dynamics and machine learning
  • Distance learning and knowledge sharing
  • Knowledge representation models
  • Management of enterprise knowledge versus personal knowledge
  • Knowledge managers and workers
  • Knowledge coaching and diffusion 
  • Knowledge engineering and software engineering
  • Managerial knowledge evolution with focus on managing of best practice and cooperative activities
  • Knowledge grid and social networks
  • Knowledge management for design, innovation and eco-innovation process
  • Business Intelligence environment for supporting knowledge management
  • Knowledge management in virtual advisors and training 
  • Management of the innovation and eco-innovation process
  • Human-machine interfaces and knowledge visualization

Thematic Track organizers

Submission rules

  • Authors should submit their papers as Postscript, PDF or MSWord files.
  • The total length of a paper should not exceed 12 pages IEEE style (including tables, figures and references). More pages can be added, for an additional fee. IEEE style templates are available here.
  • Papers will be refereed and accepted on the basis of their scientific merit and relevance to the Topical Area.
  • Preprints containing accepted papers will be published online.
  • Only papers presented at the conference will be published in Conference Proceedings and submitted for inclusion in the IEEE Xplore® database.
  • Conference proceedings will be published in a volume with ISBN, ISSN and DOI numbers and posted at the conference WWW site.
  • Conference proceedings will be submitted for indexation according to information here.
  • Organizers reserve right to move accepted papers between FedCSIS Tracks.
  • Extended versions of selected papers will be published as Special Issue(s).

History

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Important dates

  • Thematic Track proposal submission: November 28, 2023
  • Paper submission (no extensions): May 28, 2024
  • Position paper submission: June 11, 2024
  • Author notification: June 25, 2024
  • Final paper submission, registration: July 23, 2024
  • Conference date: September 8–⁠11, 2024

FedCSIS 2024 is organized by