Hierarchical Graph Embedding for Efficient Query Processing in Very Large Traffic Networks

We present a novel graph embedding to speed-up distance-range and k-nearest neighbor queries on static and/or dynamic objects located on a (weighted) graph that is applicable also for very large networks. Our method extends an existing embedding called reference node embedding which can be used to compute accurate lower and upper bounding filters for the true shortest path distance. In order to solve the problem of high storage cost for the network embedding, we propose a novel concept called hierarchical embedding that scales well to very large traffic networks. Our experimental evaluation on several real-world data sets demonstrates the benefits of our proposed concepts, i.e. efficient query processing and reduced storage cost, over existing work.