With the development of large-scale network environments and big data-related technologies, traditional data fusion analysis technology faces new challenges. Focusing on poor flexibility and low processing efficiency in current multi-source data fusion analysis process, a multi-source data parallel preprocessing method based on similar connection was proposed, in which the idea of dividing and conquering and paralleling was adopted. Firstly, the preprocessing method was improved to increase the flexibility by unifying similar semantics in multi-source data and retaining personality semantics. Secondly, an improved parallel MapReduce framework was proposed to improve the efficiency of similar connections. The experimental results show that the proposed method reduces total data volume by 32% while ensuring data integrity. Compared with traditional MapReduce framework, the improved framework decreases 43.91% of time consumed; therefore, the proposed method can effectively improve the efficiency of multi-source data fusion analysis.

Focusing on the data lost or unavailable reference in cloud storage, a secure cloud storage method based on Three-Dimensional model (TD-model)was proposed. Firstly, base nodes of TD-model method were formed by encoding the data, which would be stored uniformly into two opposite sides in the TD-model. Secondly, normal nodes were formed in each side by mathematical computing, and the nodes of each side ensure connection. Finally, high data availability was achieved by the correlation of all the six sides. The experimental results show that compared with the traditional replica storage methods, the secure cloud storage method based on TD-model enhances data recovery efficiency and ensures data integrity. In addition, the proposed method can overcome the drawback of traditional methods that only the single node failure can be recovered.