FIA5: A customized Fuzzy Interval Algebra for modeling spatial relevancy in urban context-aware systems

Abstract

Spatial relevancy is one of the primary types of relevancies that determine whether a context is spatially related to the user or not. This paper specifically addresses the use of fuzzy spatial relationships for detecting spatially relevant contexts. The proposed approach is restricted to the urban network and assumes that in such an environment, the user relates to contexts via linear fuzzy spatial intervals. The main contribution of this work is that the proposed model applies customized Fuzzy Interval Algebra (FIA5) and the Range Neighbour Query (RNQ) to introduce spatially relevant contexts according to their arrangement in space based on the position and direction of the user. The Fuzzy Spatial Relevancy Algorithm for Context-Aware Systems (FSRACAS) helps the tourist to find his/her preferred areas that are spatially relevant. The experimental results in a scenario of tourist navigation are evaluated with respect to the accuracy of the model, performance time and satisfaction of users in 100 iterations of the algorithm on 100 routes in Tehran. The evaluation process demonstrated the efficiency of the model in real-world applications.