Community search aims to find highly cohesive connected subgraphs containing user query vertices in information networks. Cycle truss is a community search model based on cycle triangle. However， the existing index-based cycle truss community search methods suffer from the drawbacks of large index space， low search efficiency， and low community cohesion. A maximum cycle truss community search method based on hierarchical tree index was proposed to address this issue. Firstly， a k-cycle truss decomposition algorithm was proposed， and two important concepts， cycle triangle connectivity and k-level equivalence were introduced. Based on k-level equivalence， the hierarchical tree index TreeCIndex and the table index SuperTable were designed. On this basis， two efficient cycle truss community search algorithms were proposed. The proposed algorithms were compared with existing community search algorithms based on TrussIndex and EquiTruss on four real datasets. The experimental results show that the space consumptions of TreeCIndex and SuperTable are at least 41.5% lower and the index construction time is 8.2% to 98.3% lower compared to TrussIndex and EquiTruss； furthermore， the efficiencies of searching for maximum cycle truss communities is increased by one and two orders of magnitude.

Aiming at the problem of high matching cost in the existing complex event matching processing methods， a complex event matching algorithm ReCEP was proposed， which uses event buffers （ordered event lists） for recursive traversal. Different from the existing method that uses automaton to match on the event stream， this method decomposes the constraints in the complex event query mode into different types， and then recursively verifies the different constraints on the ordered list. Firstly， according to the query mode， the related event instances were cached according to the event type. Secondly， the query filtering operation was performed to the event instances on the ordered list， and an algorithm based on recursive traversal was given to determine the initial event instance and obtain candidate sequence. Finally， the attribute constraints of the candidate sequence were further verified. Experimental testing and analysis results based on simulated stock transaction data show that compared with the current mainstream matching methods SASE and Siddhi， ReCEP algorithm can effectively reduce the processing time of query matching， has overall performance better than both of the two methods， and has the query matching efficiency improved by more than 8.64%. It can be seen that the proposed complex event matching method can effectively improve the efficiency of complex event processing.

Clustering results of the pSCAN （pruned Structural Clustering Algorithm for Network） algorithm are influenced by the density constraint parameter and the similarity threshold parameter. If the requirements cannot be satisfied by the clustering results obtained by the clustering parameters provided by the user， then the user’s own clustering requirements can be expressed through instance clusters. Aiming at the problem of instance clusters expressing clustering query requirements， an instance cluster-driven structural graph clustering parameter calculation algorithm PART and its improved algorithm ImPART were proposed. Firstly， the influences of two clustering parameters on the clustering results were analyzed， and correlation subgraph of instance cluster was extracted. Secondly， the feasible interval of the density constraint parameter was obtained by analyzing the correlation subgraph， and the nodes in the instance cluster were divided into core nodes and non-core nodes according to the current density constraint parameter and the structural similarity between nodes. Finally， according to the node division result， the optimal similarity threshold parameter corresponding to the current density constraint parameter was calculated， and the obtained parameters were verified and optimized on the relevant subgraph until the clustering parameters that satisfy the requirements of the instance cluster were obtained. Experimental results on real datasets show that a set of effective parameters can be returned for user instance clusters by using the proposed algorithm， and the proposed improved algorithm ImPART is more than 20% faster than the basic algorithm PART， and can return the optimal clustering parameters that satisfy the requirements of instance clusters quickly and effectively for the user.

In order to solve the problem of trajectory privacy leakage caused by the collection of numerous trajectory information of moving objects， a dummy trajectory-based trajectory privacy protection algorithm was proposed. In this algorithm， considering the user’s locations under disclosure， a heuristic rule was designed based on the comprehensive measure of trajectory similarity and location diversity to select the dummy trajectories， so that the generated dummy trajectories were able to effectively hide the real trajectory and sensitive locations. Besides， the trajectory directed graph strategy and the grid-based map strategy were proposed to optimize the execution efficiency of the algorithm. Experimental results on real trajectory datasets demonstrate that the proposed algorithm can effectively protect the real trajectory with high data utility.