A new approach to process top-k spatial preference queries in a directed road network

Top-k spatial preference query ranks objects based on the score of feature objects in their spatial neighborhood. Top-k preference queries are crucial for wide range of location based services such as hotel browsing and apartment searching; several algorithms have been proposed to process them in Euclidean space. Although, few algorithms study top-k preference queries in a road network, however, they all focus on undirected road network. To the best of our knowledge, this is the first attempt to investigate the problem of processing the top-k spatial preference queries in a directed road networks. Computation of data object score requires examining the scores of feature objects in its spatial neighborhood. This may raise the processing cost resulting in high query processing time. Therefore, in this paper we propose a new preference query search algorithm called PSA that can efficiently answer the top-k spatial preference queries in directed road network. Experimental study shows that our algorithm significantly reduces the query processing time compared to baseline solution for a wide range of problem settings.