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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (6): 135-144    DOI: 10.11925/infotech.2096-3467.2020.1006
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A Multiple Pattern Matching Algorithm for Specifications of Incremental Metadata for Sci-Tech Literature
Dong Mei1,2,Chang Zhijun1,2,Zhang Runjie3()
1National Science Library, Chinese Academy of Sciences, Beijing 100190, China
2Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
3Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
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Abstract  

[Objective] This paper designs a multiple pattern matching algorithm to standardize the institutional information of sci-tech literature metadata. [Methods] First, we used the Hash function to locate the pattern strings and reduced the system memory usage. Then, we extracted the first words of the pattern strings, which were combined with word skipping matching. The new algorithm reduced the number of matches and increased the jump range, which improved the efficiency of multiple pattern matching. [Results] We examined our model with the CSCD’s institutional library as the pattern string set. Compared with the Aho-Corasick (AC) algorithm, our method quickly constructed the dictionary corresponding to the pattern string sets. When the data volume reached about 10 000, our model spent less time on the same tasks. For the English corpus, there was a 9.39% improvement in time performance. Compared with the Wu-Manber (WM) algorithm, our method was not restricted by the shortest pattern strings. [Limitations] The algorithm or data needs to be adjusted for different pattern strings and text strings. This algorithm and the extended headless mode are not suitable for small pattern string sets with large string sets. [Conclusions] The algorithm can be applied to Chinese, English, and Chinese-English mixed texts. The time performance of our algorithm is superior to the AC and WM algorithms in processing large pattern string set (106) and small string set (about 10,000).

Key wordsPattern Match      Data Standardization      Name Authority      Hash Algorithm     
Received: 14 October 2020      Published: 29 June 2021
ZTFLH:  TP391  
Fund:Literature and Information Capacity Building Special Project of Chinese Academy of Sciences(Y9100901)
Corresponding Authors: Zhang Runjie     E-mail: 2757809002@qq.com

Cite this article:

Dong Mei,Chang Zhijun,Zhang Runjie. A Multiple Pattern Matching Algorithm for Specifications of Incremental Metadata for Sci-Tech Literature. Data Analysis and Knowledge Discovery, 2021, 5(6): 135-144.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.1006     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I6/135

Algorithm Flow Chart
Dictionary Storage Process
Pattern Matching Process of T1
first_word in FWHash start_pos stop_
pos
in Hash output
TRUE 0 1
2
3
4
5
6
7
8
FALSE
FALSE
FALSE
TRUE
FALSE
FALSE
FALSE
TRUE



中山大学



中山大学岭南学院
FALSE
FALSE
FALSE
TRUE 4 5
6
7
8
FALSE
FALSE
FALSE
TRUE



岭南学院
FALSE
FALSE
FALSE
Pattern Matching Process of T1
Pattern Matching Process of T2
first_word in FWHash start_
pos
stop_
pos
in Hash Output
Longhna FALSE
Hospital FALSE
, FALSE
Shanghai TRUE 17 26
37
40
52
60
69
FALSE
TRUE
FALSE
FALSE
FALSE
TRUE

Shanghai University



Shanghai University of Traditional Chinese
Medicine
University FALSE
of FALSE
Traditional FALSE
Chinese FALSE
Medicine FALSE
Pattern Matching Process of T2
测试数据量/条 AC算法耗时/ms 本文算法耗时/ms 提升百分比
143 3 359.16 2 164.81 35.56%
1 430 3 364.56 2 298.49 31.69%
9 724 3 397.46 3 048.69 10.27%
10 010 3 389.46 3 071.28 9.39%
11 011 3 392.46 3 166.77 6.65%
12 012 3 395.56 3 307.59 2.59%
13 013 3 398.96 3 393.75 0.15%
14 300 3 402.66 3 391.73 0.32%
Algorithm Performance on English Corpus
测试数据量/条 AC算法耗时/ms 本文算法耗时/ms 提升百分比
5 972 3 395.66 3 024.54 10.93%
9 000 3 408.46 3 245.55 4.78%
10 000 3 418.26 3 415.43 0.08%
11 945 3 436.26 4 017.76 -16.92%
Algorithm Performance on Chinese-English Mixed Corpus
Time Cost Between the Algorithm in This Paper and the WM Algorithm
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