In the era of big data and cloud computing， querying and analyzing temporal big data faces many important challenges. Focused on the issues such as poor query performance and ineffective utilization of indexes for temporal aggregation range query， a Distributed Temporal Index （DTI） for temporal aggregation range query was proposed. Firstly， random or round-robin strategy was used to partition the temporal data. Secondly， intra-partition index construction algorithm based on timestamp’s bit array prefix was used to build intra-partition index， and partition statistics including time span were recorded. Thirdly， the data partitions whose time span overlapped with the query time interval were selected by predicate pushdown operation， and were pre-aggregated by index scan. Finally， all pre-aggregated values obtained from each partition were merged and aggregated by time. The experimental results show that the execution time of intra-partition index construction algorithm of the index for processing data with density of 2 400 entries per unit of time is similar to the execution time for processing data with density of 0.001 entries per unit of time. Compared to ParTime， the temporal aggregation range query algorithm with index takes at least 22% less time for each step when querying the data in the first 75% of timeline and at least 11% less time for each step when executing selective aggregation. Therefore， the algorithm with index is faster in most temporal aggregate range query tasks and its intra-partition index construction algorithm is capable to solve data sparsity problem with high efficiency.

Knowledge Tracing （KT） is a fundamental and challenging task in online education， and it involves the establishment of learner knowledge state model based on the learning history； by which learners can better understand their knowledge states， while teachers can better understand the learning situation of learners. The KT research for learners of online education was summarized. Firstly， the main tasks and historical progress of KT were introduced. Subsequently， traditional KT models and deep learning KT models were explained. Furthermore， relevant datasets and evaluation metrics were summarized， alongside a compilation of the applications of KT. In conclusion， the current status of knowledge tracing was summarized， and the limitations and future prospects for KT were discussed.

Small Open Reading Frames （sORFs） in long non-coding RNA （lncRNA） can encode short peptides with length no more than 100 amino acids. Aiming at the problem that the features of sORFs in lncRNA are not distinct and the data with high reliability are not enough in short peptide prediction research， a Deep Forest （DF） model based on representation learning was proposed. Firstly， the conventional lncRNA feature extraction method was used to encode the sORFs. Secondly， the AutoEncoder （AE） was used to perform representation learning to obtain highly efficient representation of the input data. Finally， a DF model was trained to predict the short peptides encoded by lncRNA. Experimental results show that the accuracy of this model can achieve 92.08% on Arabidopsis thaliana dataset， which is higher than those of the traditional machine learning models ， deep learning models and combined models， and this model has better stability. In addition， the prediction accuracy of this method can reach 78.16% and 74.92% on Glycine max and Zea mays datasets respectively， verifying the good generalization ability of the proposed model.

To eliminate the most common problem of the flooding of Address Resolution Protocol (ARP) messages in Ethernet, a new ARP proxy mechanism, taking account of hybrid environment of Software-Defined Network (SDN) and traditional network was proposed environment. In this mechanism, the advantage of network-wide view of SDN paradigm was used to register hosts information once accessing into the network and update the records in real-time by keeping trace of hosts' dynamics and network failure. Thus, most ARP request messages could be responded directly by the controller. The evaluation results show that this proposed scheme reserves the auto-configuration characteristic, which is transparent to hosts, compatible with the existed hardware without any changes, reduces network traffic and allows redundant links existed in network, so as to improve the scalability of the Ethernet.

When building up the virtual battlefield system, to realize the three-dimensional (3D) detection range of radar in complex natural environment and complex environment of electronic interference, an improved support jamming model was proposed, according to the fundamental principle of Advanced Propagation Model (APM) and taking full consideration of the influence of electronic interference. This model mixed APM and electronic interference model together, and paid special attention to the refractive influence. Besides, this model could depict the double influence from complex natural and electronic interfering environments. Furthermore, a modified Marching Cube (MC) model, the triangles gained by MC being replaced by surface points and the interpolated points by middle points, was used to accelerate the process of visualization. According to the procedure of data gaining, data processing and data rendering, the 3D detection range of radar on specific electronic jamming environment was rendered via Visualization Toolkit (VTK).