工业模型检索的优化方法研究

Q3 Arts and Humanities
Icon Pub Date : 2023-03-01 DOI:10.1109/ICNLP58431.2023.00083
Wang Peng, Chunhui Hu
{"title":"工业模型检索的优化方法研究","authors":"Wang Peng, Chunhui Hu","doi":"10.1109/ICNLP58431.2023.00083","DOIUrl":null,"url":null,"abstract":"In the retrieval process of industrial models, the traditional database retrieval can no longer meet their needs in terms of efficiency and precision because of their multi-source heterogeneous, complex types and large information scale. This paper optimizes the Elasticsearch search engine in three aspects: the underlying index of the search engine, keyword search and sorting algorithm; and verifies the feasibility of the method through experiments.","PeriodicalId":53637,"journal":{"name":"Icon","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on Optimization Methods for Industrial Model Retrieval\",\"authors\":\"Wang Peng, Chunhui Hu\",\"doi\":\"10.1109/ICNLP58431.2023.00083\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the retrieval process of industrial models, the traditional database retrieval can no longer meet their needs in terms of efficiency and precision because of their multi-source heterogeneous, complex types and large information scale. This paper optimizes the Elasticsearch search engine in three aspects: the underlying index of the search engine, keyword search and sorting algorithm; and verifies the feasibility of the method through experiments.\",\"PeriodicalId\":53637,\"journal\":{\"name\":\"Icon\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Icon\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNLP58431.2023.00083\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Arts and Humanities\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Icon","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNLP58431.2023.00083","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Arts and Humanities","Score":null,"Total":0}
引用次数: 0

摘要

在工业模型的检索过程中,由于其多源异构、类型复杂、信息规模大,传统的数据库检索方法在效率和精度上已不能满足其需要。本文从三个方面对Elasticsearch搜索引擎进行优化:搜索引擎的底层索引、关键词搜索和排序算法;并通过实验验证了该方法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Optimization Methods for Industrial Model Retrieval
In the retrieval process of industrial models, the traditional database retrieval can no longer meet their needs in terms of efficiency and precision because of their multi-source heterogeneous, complex types and large information scale. This paper optimizes the Elasticsearch search engine in three aspects: the underlying index of the search engine, keyword search and sorting algorithm; and verifies the feasibility of the method through experiments.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Icon
Icon Arts and Humanities-History and Philosophy of Science
CiteScore
0.30
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信