Top-k 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.

A reverse k nearest neighbor (RkNN) query retrieves all the data points that have q as one of their k closest points. In recent years, considerable research has been conducted into monitoring reverse k nearest neighbor queries. In this paper, we study the problem of continuous reverse nearest neighbor queries where both the query object q and data objects are moving. Existing state-of-the-art techniques are sensitive towards the movement of data objects, e.g., a candidate object must be verified whenever it changes its location. Further, insufficient attention has been given to the monitoring of RNN queries in dynamic road networks where the network weight changes depending on the traffic conditions. In this paper, we address these problems by proposing a new safe exit-based algorithm called CORE-X for efficiently computing the safe exit points of both query and data objects. The safe exit point of an object indicates the point at which its safe region and non-safe region meet, thus a set of safe exit points represents the border of the safe region. Within the safe region, the query result remains unchanged provided the query and data objects remain inside their respective safe regions. The results of extensive experiments conducted using real road maps indicate that the proposed algorithm significantly reduces communication and computation costs compared to the state-of-the-art algorithm.