To solve the user similarity between trajectories formed by mobility data, an algorithm based on Location Sequence Generalized Suffix Tree (LSGST) was proposed. First, the location sequence was extracted from mobility data. At the same time the location sequence was mapped to a string. The transformation from the processing of location sequence to the processing of string was completed. Then the location sequence generalized suffix tree between different users was constructed. The similarity was calculated in detail from the number of similar positions, longest common subsequence and the frequent common position sequence. The theoretical analysis and simulation results show that the proposed algorithm has ideal effect in terms of similarity measure. Besides, compared to the ordinary construction method, the proposed algorithm has low time complexity. In the comparison with dynamic programming and naive string-matching, the proposed algorithm has higher efficiency when searching for the longest common sub-string and frequent public position sequence. The experimental results indicate that the LSGST can measure the similarity effectively, meanwhile reduces the trajectory data when searching for the measurement index, and has better performance in time complexity.

To solve the low efficiency and precision of the traditional methods, a near-duplicate image detection algorithm based on Bag-of-words and Hash coding was proposed in this paper. Firstly, a 500-dimensional feature vector was used to represent an image by Bag-of-words; secondly, feature dimension was reduced by Principal Component Analysis (PCA) and Scale-Invariant Feature Transform (SIFT) and features were encoded by Hash coding; finally, dynamic distance metric was used to detect near-duplicate images. The experimental results show that the algorithm based on Bag-of-words and Hash coding is feasible in detecting near-duplicate images. This algorithm can achieve a good balance between precision and recall rate: the precision rate can reach 90%-95%, and entire recall rate can reach 70%-80%.