Research On Path Nearest Neighbor With Multiple Types Of Data Object In Road Networks

k nearest neighbor(kNN) query is one of the fundamental issues in spatial database research area. It is designed to find the k closest objects to a specified query point q. In road networks, its variants include Continuous k Nearest Neighbor query, Aggregate Nearest Neighbor query, k Path Nearest Neighbor query, etc. Path Nearest Neighbor stresses the problem of path:when the user is moving, it may have preferred path. Path Nearest Neighbor is to find the kNN with respect to the path.In the problem of PNN, there is one type of object, but in real application, there may be different type of object that user may be interested in. This thesis is just to resolve this problem.k Path Nearest Neighbor is well introduced in this thesis. A query to k path nearest neighbor with two types of data objects in road networks is proposed, it is a new type of query:given a destination where a user is going to and two sets of data objects, this query returns the shortest path connecting the destination and the user’s current location, and the kPNN with two types of data objects, We name this query kPNNT. We propose a filter-refine algorithm to get kPNNT and the corresponding shortest path, also we use preprocess method to improve the algorithm. Then we propose k path nearest neighbor with multiple types of data object in road networks, also we propose a preprocess method to solve this problem.At last, we conduct extensive experiment on real road networks.These algorithms are proved to be efficient according to the experimentation.