海量模式高效匹配方法研究

Massive Strings Efficient Matching Method Research

本文提出了一种基于随机指纹模型的Wu and Manber(WM)算法(Randomizing Fingerprint WM,RFPWM),它通过为每一个模式串计算唯一指纹可以有效降低误报率.与WM算法相比,RFP-WM算法极大地降低了哈希冲突率,提高了命中率,在海量模式集上这一效果更为显著.实验结果表明,相对于传统WM算法,该算法的匹配效率更高,而且模式集的规模越大,性能越优越.

This paper presents a randomizing fingerprint-based Wu and Manber（WM） algorithm（RFP-WM） ,which can ef-fectively reduce false positives rate by calculating a unique fingerprint for each pattern .Compared with WM algorithm ,RFP-WM al-gorithm greatly reduces the hash collision rate and increases the hit rate ,especially in the massive patterns set .Experiment results show that the performance of the RFP-WM algorithm is more superior than traditional Wu and Manber （WM） algorithm on the larg-er pattern set .