To solve the time-consuming problem of group-by aggregation operation in case of data-intense computation, a cache-friendly group-by aggregation method was proposed. In this paper, the group-by aggregation operation was optimized in two aspects. Firstly, designing cache-friendly group-by aggregation algorithm on Supersonic, an open-source and column-oriented query execution engine, to take the full advantage of column-storage on in-memory computation. Secondly, rewriting the algorithm with multi-threads to speed up the query. In this paper, four different parallel aggregation algorithms were put forward, respectively named Shared-Nothing Parallel Group-by Aggregation (NSHPGA) algorithm, Table-Lock Shared-Hash Parallel Group-by Aggregation (TLSHPGA) algorithm, Bucket-Lock Shared-Hash Parallel Group-by Aggregation (BLSHPGA) algorithm and Node-Lock Shared-Hash Parallel Group-by Aggregation (NLSHPGA) algorithm. Through a series of comparison experiment on different group power set and different number of worker threads, NLSHPGA algorithm was proved to have the best performance both on speed-up ratio and concurrency, which achieved 10x speedups on part of queries. Besides, considering Cache miss and memory utilization, the results shows that NSHPGA algorithm is suitable for smaller group power set, which was 8 in the experiment, and when getting larger, NLSHPGA algorithm performs better than NSHPGA algorithm.

Image registration is a well researched topic of computer vision. To deal with matching efficiency, repetitive pattern matching and affine invariant matching better, two improvements over the state-of-the-art Affine-Scale Invariant Feature Transform (ASIFT) algorithm were presented. The feature extraction of matching frame was developed to improve the matching efficiency of the ASIFT algorithm. The second increased the accuracy of matching and the adaptive capacity of repetitive patterns through the use of improved matching algorithm by combining Optimized Random Sample Consensus (ORSA) with Random Sample Consensus (RANSAC) algorithm based on geometric linear constraint model with homography matrix. The experimental results show that the proposed method is able to well match highly repetitive patterns and has smaller calculation, faster speed and higher accuracy as well.

Polymorphic worms signature extraction is a critical part of signature-based intrusion detection. Extracting precise signatures quickly plays an important role in preventing the spread of the worms. Since the classical Hierarchical Multi-Sequence Alignment (HMSA) algorithm has bad time performance in extracting signatures when multiple sequences alignment was used and the extracted signatures were not precise enough, a new automatic signature extraction method called antMSA was proposed based on the improved ant optimal algorithm. The search strategy of the ant group was improved, and then it was introduced to the Contiguous Matches Encouraging Needleman-Wunsch (CMENW) algorithm to get a better solution quickly in global range by using the rapid convergence ability of ant colony algorithm. The signature fragments were extracted and converted into the standard rules of the intrusion detection system for subsequent defense. The experimental results show that the new method solves the stagnation problem of the classical ant optimal algorithm, extends the search space, extracts signatures more efficiently and precisely, and reduces the false positive rate and the false negative rate.