More Efficient Filtration Method for Big Data Order-Preserving Matching

Wenchao Jiang, Dexi Lin, Sui Lin, Chuanjie Li, Aobing Sun

Abstract: Data matching and retrieval aims at finding out similar substrings with the pattern P in the given data set T. This problem has wide applications in big data analysis. A liberalized verification rule is proposed first, and then a similarity computing based order preserving matching method is presented. Theory analysis indicates our method runs in linear. Furthermore, the experimental results show that our method can improve effectively the precision ratio and the recall ratio. More qualified matching results can be detected compared with the state of the art of this problem.