Considering the road network constraint and the uncertainty of moving object location, a new reverse-kNN query on road network termed Probabilistic Bichromatic Reverse-kNN (PBRkNN) was proposed to find a set of uncertain points and make the probability which the kNN of each uncertain point contains the given query point be greater than a specified threshold. Firstly, a basic algorithm called Probabilistic Eager (PE) was proposed, which used Dijkstra algorithm for pruning. Then, the Pre-compute Probabilistic Eager (PPE) algorithm which pre-computes the kNN for each point was proposed to improve the query efficiency. In addition, for further improving the query efficiency, the Pre-compute Probabilistic Eager External (PPEE) algorithm which used grid index to accelerate range query was proposed. The experimental results on the road networks of Beijing and California show that the proposed pre-computation strategies can help to efficiently process probabilistic bichromatic reverse-kNN queries on road networks.

Group Trip Planning (GTP) queries are targeting at finding some same activity sites for a group of users (usually expressed as Point of Interests (PoI)), in ordor to minimize the total travel cost. Existing researches on GTP queries are limited in Euclidean space, however, real travel is restricted by road network. Motivated by this observation, two algorithms (NE-GTP and ER-GTP) were designed to solve the GTP queries. NE-GTP expanded the network around every user's location to iteratively find the PoI, while ER-GTP used R-tree index and Euclidean distance to quickly get the results. The experimental results show that ER-GTP always performs on average an order of magnitude processing time faster than NE-GTP. In addition, when the dataset becomes large, ER-GTP also has good scalability.

Most supervised classification methods require large training samples to avoid the well-known Hughes effect. However, labeling samples is often very expensive in actual world applications. In order to reduce the number of training samples, high-quality training samples are extremely important. A hyperspectral image classification based on active learning was proposed. It provided a new calculation method for concerning region attention degree to combine spectral and spatial characteristics of the image effectively, and used active learning method to obtain training set with the most abundant information and improved the classification accuracy ultimately. The experimental results show that the proposed method performs particularly well for the classification of hyperspectral images, when compared to random sampling supervised classification method and active learning approaches.