A Cumulative Running Work (CRW) based task scheduler CRWScheduler was proposed to effectively process tasks without any prior knowledge for big data analytics platform like Hadoop and Spark. The running job was moved from a low-weight queue to a high-weight one based on CRW. When resources were allocated to a job, both the queue of the job and the instantaneous resource utilization of the job were considered, significantly improving the overall system performance without prior knowledge. The prototype of CRWScheduler was implemented based on Apache Hadoop YARN. Experimental results on 28-node benchmark testing cluster show that CRWScheduler reduces average Job Flow Time (JFT) by 21% and decreases JFT of 95th percentile by up to 35% compared with YARN fair scheduler. Further improvements can be obtained when CRWScheduler cooperates with task-level schedulers.

Mining outliers is to find exceptional objects that deviate from the most rest of the data set. Outlier detection based on density has attracted lots of attention, but the density-based algorithm named Local Outlier Factor (LOF) is not suitable for the data set with abnormal distribution, and the algorithm named INFLuenced Outlierness (INFLO) solves this problem by analyzing both k nearest neighbors and reverse k nearest neighbors of each data point at cost of inferior efficiency. To solve this problem, a local density-based algorithm named Local Density Based Outlier detection (LDBO) was proposed, which can improve outlier detection efficiency and effectiveness simultaneously. LDBO introduced definitions of strong k nearest neighbors and weak k nearest neighbors to realize outlier relation analysis of those data points located nearby. Furthermore, to improve the outlier detection efficiency, prejudgement was applied to avoid unnecessary reverse k nearest neighbor analysis as far as possible. Theoretical analysis and experimental results Indicate that LDBO outperforms INFLO in efficiency, and it is effective and feasible.

Concerning the limitations of person safety and difficulties in nodes repair, placement, search and rescue caused by complex or unreachable special areas where the Wireless Sensor Network (WSN) deployed in, an autonomous mobile strategy of carrier in WSN was proposed. Firstly, the localization of the carrier with fewer anchor nodes was realized by combining the maximum likelihood method and Received Signal Strength Indication (RSSI). Then, relying on the mathematical model, carrier moved autonomously in WSN by acquiring current position information and target node coordinates to amend the direction angle and select the next target node. The simulation results show that the proposed strategy can ensure the carrier to reach the destination along the shorter path and in less time, and the higher the density of sensor nodes is, the more likely this strategy will succeed. The WSN with 130, 180 and 300 nodes were simulated respectively, and the success rate was as high as 96.7%.

There are the errors that memory data is not updated, the loop exits early and the loop exits late in the program loop control. In order to ensure the correctness of the program execution in the safety critical system, a new error detection algorithm of program loop control based on ANBD-code (arithmetic-code with signature and timestamp) was proposed. Through ANBD-code, the program variables were encoded as a signed code word by the proposed algorithm. And the errors in the loop control were detected by verifying code signature, the error of memory data being not updated could be detected by using the time label of ANBD-code. In addition, on the basis of the ANBD-code, the errors of the loop exiting early and the loop exiting late could be detected by using the online statement block signature allocation algorithm, the block signature function and the variable signature compensation function. The occurrence probability of an undetected error was 1/ A in theory, where A was coding prime. The primes were selected between 97 and 10993 to test occurrence probability of an undetected error and the Normalized Mean Square Error (NMSE) of theoretical model and test result was about-30 dB. The test results show that the proposed algorithm can effectively detect all kinds of errors in the loop control and the occurrence probability of an undetected error is up to 10 ^-9 when the prime A is close to 2 ³². The proposed algorithm can satisfy the requirements of safety critical system.

To deal with the huge spatial and temporal consumption caused by large-scale self-set data, the authors designed a self-set matching mechanism based on artificial immune Network Intrusion Detection System (NIDS). To improve the detection efficiency of the intrusion detection system, an efficient probability matching optimization mechanism was proposed. The authors first proved the relative concentration of the network data, and analyzed the validity of the probability matching mechanism by calculating the Average Search Length (ASL), then verified the fast matching efficiency of the mechanism through simulation experiments. The mechanism has been used in a project application in a new artificial immune network intrusion detection system based on self-set scale simplified mechanism, which has achieved satisfactory matching results.

Based on the mass point method, the paper developed a new Mathematica prover. With this prover, hundreds of modern geometric theorems had been proved for the first time, and the proof readability was also satisfactory. With its help, some of the new modern geometric properties were found, and some research results on modern geometry got deepened too.