In view of the problems such as the slow computation of large flow data, the high computation pressure on the server, a set of relay computation and dynamic diversion model of computing-intensive large flow data was proposed. Firstly, in the distributed environment, the in-memory data storage technology was used to determine the computation amounts and complexity levels of the computation tasks. At the same time, the nodes were sorted by the node resource capacity, and the tasks were dynamically allocated to different nodes for parallel computing. Meanwhile, the computation tasks were decomposed by a relay processing mode, so as to guarantee the performance and accuracy requirements of high flow complex computing tasks. Through analysis and comparison, it can be seen that the running time of multiple nodes is shorter than that of the single node, and the computation speed of multiple nodes is faster than that of the single node when dealing with data volume of more than 10 000 levels. At the same time, when the model is applied in practice, it can be seen that the model can not only reduce the running time in high concurrency scenarios but also save more computing resources.

Aiming at new sentiment word identification, an automatic extraction of new words about microblog was proposed based on the word association. Firstly, a new word, which was incorrectly separated into several words using the Chinese auto-segmentation system, should be assembled as the candidate word. In addition, to make full use of the semantic information of word context, the spatial representation vector of the candidate words was obtained by training a neural network. Finally, using the existing emotional vocabulary as a guide, combining the association-sort algorithm based on vocabulary list and the max association-sort algorithm, the final new emotional word was selected from candidate words. The experimental results on the task No. 3 of COAE2014 show that the precision of the proposed method increases at least 22%, compared to Pointwise Mutual Information (PMI), Enhanced Mutual Information (EMI), Normalized Multi-word Expression Distance (NMED), New Word Probability (NWP), and identification of new sentiment word based on word embedding, which proves the effectiveness of the proposed method.

As the radio frequency communication technology gets more mature and the hardware manufacturing cost decreases, Radio Frequency IDentification (RFID) technology has been applied in the domains of real-time object monitoring, tracing and tracking. In supply chain applications, there are usually a great number of RFID objects to be monitored and traced, and objects' locations are changed essentially, so how to query the locations and the histories of location change of the RFID objects, from the huge volume of RFID data, is an urgent problem to be addressed. Concerning the characteristics of mobile RFID objects and the tracing query requirements in supply chain applications, an effective spatio-temporal index, called as CR-L, was put forward, and its structure and maintenance algorithms, including insertion, deletion, bi-splitting, and lazy splitting, were discussed in detail. In order to support object queries effectively, a new calculation principle of Minimum Bounding Rectangle (MBR), considering the three dimensional information including readers, time and objects, was presented to cluster the trajectories by the same reader at close time into the same node or the neighboring nodes. As to trajectory queries, a linked list was designed to link all trajectories belonging to the same object. The experimental results verify that CR-L has better query efficiency and lower space utilization rate than the existing method.

Dynamic trusted measurement is a hot and difficult research topic in trusted computing. Concerning the measurement difficulty invoked by the dynamic nature of operating system kernel, a Dynamic Trusted Kernel Measurement (DTKM) model was proposed. Dynamic Measurement Variable (DMV) was presented to describe and construct dynamic data objects and their relations, and the method of semantic constraint was proposed to measure the dynamic integrity of kernel components. In DTKM, the collection of memory data was implemented in real-time, and the dynamic integrity was verified by checking whether the constructed DMV was consistent with semantic constraints which were defined based on the security semantics. The nature analysis and application examples show that DTKM can effectively implement dynamic measurement of the kernel and detect the illegal modification of the kernel dynamic data.