Shared execution approach to ε-distance join queries in dynamic road networks

Given a threshold distance ε and two object sets R and S in a road network, an ε-distance join query finds object pairs from R × S that are within the threshold distance ε (e.g., find passenger and taxicab pairs within a five-minute driving distance). Although this is a well-studied problem in the Euclidean space, little attention has been paid to dynamic road networks where the weights of road segments (e.g., travel times) are frequently updated and the distance between two objects is the length of the shortest path connecting them. In this work, we address the problem of ε-distance join queries in dynamic road networks by proposing an optimized ε-distance join algorithm called EDISON, the key concept of which is to cluster adjacent objects of the same type into a group, and then to optimize shared execution for the group to avoid redundant network traversal. The proposed method is intuitive and easy to implement, thereby allowing its simple integration with existing range query algorithms in road networks. We conduct an extensive experimental study using real-world roadmaps to show the efficiency and scalability of our shared execution approach.