Abstract
Reverse k-nearest neighbor (RkNN) queries on road network distances require long processing times because most conventional algorithms require a k-nearest neighbor (kNN) search on every visited node. This causes a large number of node expansions; therefore, the processing time is drastically increased when data points are sparsely distributed. In this paper, we propose a fast RkNN search algorithm that runs using a simple materialized path view (SMPV). In addition, we adopt an incremental Euclidean restriction strategy for fast kNN queries, the main function in RkNN queries. The SMPV used in our proposed algorithm only constructs an individual partitioned subgraph; therefore, the amount of data is drastically reduced compared to conventional materialized path views (MPVs). According to our experimental results using real road network data, our proposed method achieved a processing time that was 100 times faster than conventional approaches when data points are sparsely distributed on a road network.
