The frequent wayfinding-sequence (FWS) methodology: Finding preferred routes in complex virtual environments

  • Authors:

  • Pedram Sadeghian;Mehmed Kantardzic;Oleksandr Lozitskiy;Walaa Sheta

  • Affiliations:

  • CECS Department, University of Louisville, Louisville, KY, 40292 USA;CECS Department, University of Louisville, Louisville, KY, 40292 USA;CECS Department, University of Louisville, Louisville, KY, 40292 USA;Informatics Research Institute, Mubarak City for Scientific Research, Alexandria, Egypt

  • Venue:
  • International Journal of Human-Computer Studies
  • Year:
  • 2006

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Abstract

Advances in computing techniques, as well as the reduction in the cost of technology, have made possible the viability and spread of complex virtual environments (VEs). However, efficient navigation within these environments remains problematic for the user. Several research projects have shown that users of VEs are often disoriented and have extreme difficulty completing navigational tasks. Furthermore, there is often more than one route to get to a specified destination. Novice users often lack the spatial knowledge needed to pick an appropriate route due to the deficiency of experience with the system. A number of navigation tools such as maps, 3-D thumbnails, trails, and personal agents have been proposed. The introduction of these tools have met with some degree of success, but most researchers agree that new techniques need to be developed to aid users efficiently navigate within complex VEs. In this paper, we propose the frequent wayfinding-sequence (FWS) methodology that uses a modified sequence mining technique to discover a model of routes taken by experienced users of a VE. The model is used to build an interface that provides navigation assistance to novice users by recommending routes. We conducted both real world and simulation experiments using our methodology. Results from the real world experiment suggest that the FWS approach has the potential to improve the user's navigation performance and the quality of the human-computer interaction. Our simulation studies showed that our approach is scalable, efficient, and able to find useful route models for complex VEs.