spatial preference queries rank objects based on the score of feature objects in their spatial neighborhood. Top-k
preference queries are crucial for a wide range of location based services such as hotel browsing and apartment searching. In recent years, a lot of research has been conducted on processing of top-k
spatial preference queries in Euclidean space. While few algorithms study top-k
preference queries in road networks, they all focus on undirected road networks. In this paper, we investigate the problem of processing the top-k
spatial preference queries in directed road networks where each road segment has a particular orientation. Computation of data object scores requires examining the scores of each feature object in its spatial neighborhood. This may cause the computational delay, thus resulting in a high query processing time. In this paper, we address this problem by proposing a pruning and grouping of feature objects to reduce the number of feature objects. Furthermore, we present an efficient algorithm called TOPS that can process top-k
spatial preference queries in directed road networks. Experimental results indicate that our algorithm significantly reduces the query processing time compared to period solution for a wide range of problem settings.
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