{"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":"113 1","pages":"425-433"},"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\":\"113 1\",\"pages\":\"425-433\"},\"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}
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.