The problems of top-K optimal route query with keyword search support is a route query with given road network， a set of points of interest， a starting point and multiple keywords. The goal of query is to find k optimal routes that pass through multiple points of interest matching the query keywords. However， some existing research simplified the algorithm by using the order of user input keywords as the order of reaching points of interest， which is not suitable for scenarios where there is no requirement for the order of reaching points of interest， thereby reducing practicality. Additionally， some research aims to enhance query efficiency by setting distance thresholds to prune points of interest that do not meet the requirements， but such algorithms cannot guarantee that the pruned points of interest cannot form the optimal route. To address the problems of the above algorithms， a Keyword-aware top-K optimal Routes Search （KKRS） algorithm was proposed. Firstly， the entire road network was divided into multiple subnetworks. Then， a heuristic search strategy was employed to gradually expand the search scope starting from the subnetwork within query’s starting point until the top-K optimal routes were found or the entire road network was traversed. During the expansion process， a subgraph pruning strategy was introduced to remove subnetworks that do not contain the top-K optimal routes， thus reducing the search scope. Furthermore， to avoid computing each potentially optimal set of points of interest one by one， a pruning strategy for the sequence of points of interest was designed to quickly filter out those sequences that cannot form the optimal route， thereby reducing the computational cost. Finally， experiments were conducted on real and synthetic datasets with the two proposed pruning algorithms. These two algorithms achieved the pruning rates of subgraph pruning over 70%， and the pruning rates of points of interest sequence pruning ensured over 60% on all datasets. Compared to the advanced algorithms Keyword-aware Optimal Route query on Large-scale Road Networks （KORL）， ROSE-GM （Recurrent Optimal Subroute Expansion using Greedy Merge Strategy）， OSSCaling， and StarKOSR （finding Top-K Optional Sequenced Routes with A^*）， The KKRS algorithm is 40% more efficient than the StarKOSR algorithm， which is the more query efficient of compared algorithms.

The k-Nearest Neighbor （kNN） query in dynamic road network is an important problem in many Location-Based Services （LBS）. To address this problem， a distributed moving object kNN query algorithm named DkNN （Distributed k-Nearest Neighbor） was proposed for dynamic road networks. Firstly， the entire road network was divided into multiple subgraphs deployed in different nodes in the cluster. Then， the accurate kNN results were obtained by searching the subgraphs involved in the query range in parallel. Finally， the searching process of the query was optimized， and the query range pruning strategy as well as the query termination strategy was introduced. A full comparison and validation with three baseline algorithms on four road network datasets was performed. Experimental results show that the DkNN algorithm reduces the query time by 56.8% and the road network update time by 3 orders of magnitude compared to TEN^*-Index （Tree dEcomposition based kNN^* Index） algorithm. The DkNN algorithm can quickly respond to kNN query requests in dynamic road network and has a low update cost when dealing with road network updates.

Spatial-temporal co-occurrence patterns refer to the video object combinations with spatial-temporal correlations. In order to mine the spatial-temporal co-occurrence patterns meeting the query conditions from a huge volume of video data quickly， a spatial-temporal co-occurrence pattern mining algorithm with a triple-pruning matching strategy — Multi-Pruning Algorithm （MPA） was proposed. Firstly， the video objects were extracted in a structured way by the existing video object detection and tracking models. Secondly， the repeated occurred video objects extracted from a sequence of frames were stored and compressed， and an index of the objects was created. Finally， a spatial-temporal co-occurrence pattern mining algorithm based on the prefix tree was proposed to discover the spatial-temporal co-occurrence patterns that meet query conditions. Experimental results on real and synthetic datasets show that the proposed algorithm improves the efficiency by about 30% compared with Brute Force Algorithm （BFA）， and the greater the data volume， the more obvious the efficiency improvement. Therefore， the proposed algorithm can discover the spatial-temporal co-occurrence patterns satisfying the query conditions from a large volume of video data quickly.