Micro-Expression (ME) occurrence is only related to local region of face, with very short time and subtle movement intensity. There are also some unrelated muscle movements in the face during the occurrence of micro-expressions. By using existing global method of micro-expression recognition, the spatio-temporal patterns of these unrelated changes were extracted, thereby reducing the representation capability of feature vectors, and thus affecting the recognition performance. To solve this problem, the local region method was proposed to recognize micro-expression. Firstly, according to the region with the Action Units (AU) related to the micro-expression, seven local regions related to the micro-expression were partitioned by facial key coordinates. Then, the spatio-temporal patterns of these local regions were extracted and connected in series to form feature vectors for micro-expression recognition. The experimental results of leave-one-subject-out cross validation show that the micro-expression recognition accuracy of local region method is 9.878% higher than that of global region method. The analysis of the confusion matrix of each region's recognition result shows that the proposed method makes full use of the structural information of each local region of face, effectively eliminating the influence of unrelated regions of the micro-expression on the recognition performance, and its performance of micro-expression recognition can be significantly improved compared with the global region method.

The functionality of a framework that supported location-based services on moving objects in road networks was extended and Snapshot K Nearest Neighbor (SKNN) queries based on Mobile Network Distance Range (MNDR) queries was proposed using an on-disk R-tree to store the network connectivity and an in-memory grid structure to maintain the moving object position updates. The minimum and maximum number of grid cells of a given arbitrary edge in the space that were possibly affected were analyzed. The maximum bound that could be used in snapshot range query processing to prune the search space was shown. SKNN estimated the subspace containing the query results and used the subspace as range to efficiently compute the KNN POI from the query points to reduce I/O cost and time of query. Analysis shows that the maximum bound can be used in snapshot range query processing to prune the search space. The contrast experiments show that SKNN has better system throughput than S-GRID while scaling to hundreds of thousands of moving objects.

Because of the user's mobility and the location dependency of data, new challenge has been brought to cache replacement strategy for Location Dependent Query (LDQ). Based on the detailed analysis of the space location characteristics of Location Dependent Data (LDD) and several typical replacement strategies of location dependent cache, the authors proposed a prioritized approach cache replacement based on the lowest access cost (PLAC), the PLAC took some important factors into account such as access probabilities, update rates, data distance, valid scope. To ensure the maximum utilization of limited cache, the PLAC cache replacement strategy decided which data would be replaced according to the value of the lowest cost function. The contrast experiments show that the PLAC increases cache hit rate and shortens query average response time more effectively than other location dependent cache replacement strategies.