Abstract
Real-time positioning of a specific object in the big data environment can improve the monitoring and management capacity for network data. For the real-time positioning of the specific object, it is necessary to quickly search the network data representing a specific object and match its pattern strings and compare the corresponding Internet protocol (IP) address of the matched network data with the IP address library in real time, so as to determine the position of the specific object. When a traditional method is used for pattern string matching, it will occupy a lot of memories and network resources, thereby reducing the positioning effect of the specific object in the big data environment. A positioning method for a specific object of high performance and multi-pattern matching based on three indexes in the big data network environment is proposed in this paper. Firstly, the initialization of Modified Wu-Manber (MWM) algorithm was carried out, and the algorithm was used to match the network data continuously. Secondly, the three indexes were used to improve the MWM algorithm, and the real-time and fast positioning of a specific object in the big data environment was completed by the Third Index Modified Wu-Manber (TMWM). The experimental results show that compared with the traditional method, the proposed algorithm reduces the pattern string matching scope of network data representing the specific object, improves the search speed of the specific object, and locates the specific object in the big data environment in an effective and rapid manner.
Highlights
With the popularization of network application, the network scale has been expanded, the services carried in the network have become more and more abundant, and network users enjoy much more conveniences
2.1 Algorithm initialization Before Third Index Modified Wu-Manber (TMWM) matching algorithm is used, it is necessary to carry out initialization processing for the rapid research algorithm of network data, and the realization process is described as follows: Step 1: Input all pattern strings of network data representing the specific objects into a pattern string array and quickly sort them from small to large according to the size of ASCII code of character (American Standard Code for Information Interchange)
In order to verify the effectiveness of the positioning method of network data with high performance and multi-pattern matching based on pattern string TMWM proposed in this paper, the simulation experiments require to be carried out to compare with the traditional STRFINDUNI algorithm, and the main influence factors of these two algorithms include: number of pattern strings of network data, the size of text of network data, the mean length of pattern string of network data, and the hit times of network data
Summary
With the popularization of network application, the network scale has been expanded, the services carried in the network have become more and more abundant, and network users enjoy much more conveniences. 2.1 Algorithm initialization Before TMWM matching algorithm is used, it is necessary to carry out initialization processing for the rapid research algorithm of network data, and the realization process is described as follows: Step 1: Input all pattern strings of network data representing the specific objects into a pattern string array and quickly sort them from small to large according to the size of ASCII code of character (American Standard Code for Information Interchange). 2.2 Matching process of algorithm Based on the initialization processing of network data representing specific object above, the pattern strings of the network data is matched, and the detailed implementation process is described as follows: Step 1: Firstly, take the initial address of the network data as the initial address of the matching window; search for the jump table according to the suffix of the matching window in Chinese text (length of the jump block) to obtain the maximum distance of the jump searching and jump until the jump fails.
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