Open AccessResearch on a Novel Improved KMP Fuzzy Query Algorithm
Author(s) -
Bowen Ni,
Xiaomei Hu,
Chai Jaturapitakkul,
Hewei Qu,
Tao Yu
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1302/2/022034
Subject(s) - approximate string matching , computer science , query optimization , fuzzy logic , algorithm , commentz walter algorithm , data mining , matching (statistics) , query language , sargable , pattern matching , theoretical computer science , mathematics , artificial intelligence , web search query , information retrieval , search engine , statistics
The rapid development of computer technology has led to an increasing demand for database management information systems. Most of the data queries existing in the current system use accurate query methods, which leads to inefficient query and cannot solve the fuzzy matching problem between strings. Based on the Knuth-Morris-Pratt algorithm, this paper introduces the concept of ambiguity and proposes an improved KMP fuzzy query algorithm, which is applied to the disease query system to verify the feasibility of the algorithm. The improved KMP fuzzy query algorithm not only has a high matching speed between strings, but also satisfies the fuzzy matching between strings. Compared with the traditional BF algorithm, KMP algorithm and other algorithms, the improved KMP fuzzy query algorithm has superiority in terms of fuzzy matching.