[1]胡存刚,程莹. 基于粒子群算法的大数据智能搜索引擎的研究[J].计算机技术与发展,2015,25(12):14-17.
　HU Cun-gang,CHENG Ying. Research on Big Data Intelligent Search Engine Based on PSO[J].,2015,25(12):14-17.
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 基于粒子群算法的大数据智能搜索引擎的研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年12期
页码:
14-17
栏目:
智能、算法、系统工程
出版日期:
2015-12-10

文章信息/Info

Title:
 Research on Big Data Intelligent Search Engine Based on PSO
文章编号:
1673-629X（2015)12-0014-04
作者:
 胡存刚; 程莹
 安徽大学
Author(s):
 HU Cun-gang; CHENG Ying
关键词:
 ; 大数据; 粒子群算法; 数据管理平台; 智能搜索引擎
Keywords:
 ; big data; particle swarm optimization; data management platform; intelligent search engine
分类号:
TP31
文献标志码:
A
摘要:
 在开发公安机关大数据管理平台的基础上,对基于粒子群算法的智能搜索引擎进行了研究,有效地解决了目前搜索引擎不能很好地为用户提供感兴趣信息的问题且提高了搜索效率. 大数据管理平台采用倒排序分割存储方法,显著提高了分割效率. 为了避免客户端同服务器的大量数据交互,造成网络的堵塞,文中项目创新性地采用了数据同步技术,即对客户端数据库与服务器数据库的数据进行一致性更新. 建立客户端数据库,实现客户端文件树的生成. 大幅降低了客户端与服务器交互的数据量. 针对公安机关大数据的特点,将粒子群算法引入智能搜索引擎中,以实现公安大数据的关联搜索. 最后研发了一套公安机关大数据管理平台和智能搜索引擎,通过实验验证了搜索结果的正确率以及满意度.
Abstract:
 In big data management platform of public security organ,an intelligent search engine based on PSO ( Particle Swarm Optimi-zation) algorithm is studied,which solves the current search engines’ problem that cannot be very good for users to find their interested information and improves the search efficiency. Inverted sequence file segmentation storage method is adopted in big data management platform,significantly improving the efficiency of segmentation. In order to avoid a large number of data interaction between the client and the server,resulting in network congestion,use innovative data synchronization technology. The establishment of client database,gen-erate the client file tree,significantly reducing the amount of data communication between client and server. According to the characteris-tics of public security organ’s big data,the PSO algorithm is introduced to intelligent search engine. Finally,develop a big data manage-ment platform and intelligent search engine for the public security organs. And through experiments verify the accuracy of search results and satisfaction of users.

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更新日期/Last Update: 2016-01-26