Modeling On-the-Spot Learning: Storage, Landmarks Weighting Heuristic and Annotation Algorithm

TLDR: An OTSL model that includes the storage, retrieval and the landmark weighting heuristic is proposed that includes a weighting model to select the correct landmarks in the basic model and is extended to include other factors such as speed, direction, side of the road etc.

Abstract: Huge information is intrinsically associated with certain places in the globe such as historical, geographical, cultural and architectural specialties. The next generation systems require access of the site specific information where the user is roaming at the moment. The on-the-spot learning (OTSL) is a system that allows the users to learn about the location, landmarks, regions where he/she is walking through. In this paper, we have proposed an OTSL model that includes the storage, retrieval and the landmark weighting heuristic. Apart from learning about the individual landmarks, we have proposed two ways of storing the spatial learning objects. First, use the administrative hierarchy of the region to fetch the information. This can be easily done by the reverse-geocoding operation without actually storing the physical hierarchy. Second, spatial chunking, creates the region based on the groups of landmarks in order to define a learning region. A hybrid solution has also been considered to achieve the advantages of both the region based methods. We use a weighting model to select the correct landmarks in the basic model. We extend the core model to include other factors such as speed, direction, side of the road etc. A prototype has been implemented to show the feasibility of the proposed model.