The inherent chain structure of blockchain means that its data volume grows linearly and endlessly. Over time,it causes a lot of pressure on the storage of the single node,which greatly wastes the storage space of the whole system. The Simplified Payment Verification(SPV)node model proposed in the Bitcoin white paper greatly reduces the node's need for storage space. However,it reduces the number of nodes and increases the pressure,which weakens the decentralization of the entire system and has security risks such as denial of service attacks and witch attacks. By analyzing the Bitcoin block data,a fully functional enhanced lightweight node model Enhanced SPV(ESPV)was proposed. The block was divided into new blocks and old blocks by ESPV,and different storage management strategies were adopted for them. The new block was saved in full copy(one copy per node)for transaction verification,allowing ESPV to has transaction verification(mining) function with less storage space cost. The old block was stored in the nodes of the network in slices,and was accessed through the hierarchical block partition routing table,thereby reducing the waste of the storage space of the system under the premise of ensuring data availability and reliability. The ESPV nodes have full node functionality,thus ensuring the decentralization of the blockchain system and enhancing the security and stability of the system. The experimental results show that the ESPV nodes have more than 80% transaction verification rate,and the data volume and growth amount of these nodes are only 10% of those of all nodes. The data availability and reliability of ESPV are guaranteed,and it is applicable to the whole life cycle of the system.

To solve the problems that existing Progressive Mesh (PM) simplification algorithms are facing, such as, loosing key features when meshes are highly simplified, low simplification speed and limited applicability for various models, an edge-collapsing mesh simplification algorithm combining Quadric Error Metric （QEM） and curvature-like Feature value with Variable Parameter (QFVP) was proposed to build progressive meshes for mobile devices. Firstly, the variable parameter w was set to control the relative magnitude of quadratic error and curvature-like value in edge-collapsing error, improving the simplification quality of the algorithm and making the algorithm more applicable. Secondly, an error Back Propagation (BP) neural network was trained to determine the w value of the model. Thirdly, the normal vector linear estimation method in the edge-collapse process was proposed, which shortens the mesh simplification time by 23.7% on average compared to Gouraud estimation method. In the comparison experiments, the PM’s basic meshes generated by QFVP have smaller global error (measured by Hausdorff distance) than those generated by QEM algorithm or Melax algorithm. And QFVP has simplification time about 7.3% longer than QEM algorithm and 54.7% shorter than Melax algorithm.

To alleviate the issues of high complexity and poor scalability in the processing of keyword-aware optimal route query algorithms for large scale graph or multiple query keywords, an effective algorithm was proposed based on the scheme of keyword sequence route generating. The algorithm satisfied the coverage of query keywords first, and took a path expansion inspired by the keyword coverage property rather than aimless adjacent edge expansion to efficiently construct candidate paths. With the aid of a scaling method and ineffective route pruning, the search space was reduced into a polynomial order from an original factorial order, which further reduced the complexity and enhanced the scalability. Experiments conducted over four gragh datasets verified the accuracy and improvement in efficiency and scalability of the proposed algorithm.