{"title":"使用MinHash LSH搜索Web数据","authors":"B. Rao, Erkang Zhu","doi":"10.1145/2882903.2914838","DOIUrl":null,"url":null,"abstract":"In this extended abstract, we explore the use of MinHash Locality Sensitive Hashing (MinHash LSH) to address the problem of indexing and searching Web data. We discuss a statistical tuning strategy of MinHash LSH, and experimentally evaluate the accuracy and performance, compared with inverted index. In addition, we describe an on-line demo for the index with real Web data.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Searching Web Data using MinHash LSH\",\"authors\":\"B. Rao, Erkang Zhu\",\"doi\":\"10.1145/2882903.2914838\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this extended abstract, we explore the use of MinHash Locality Sensitive Hashing (MinHash LSH) to address the problem of indexing and searching Web data. We discuss a statistical tuning strategy of MinHash LSH, and experimentally evaluate the accuracy and performance, compared with inverted index. In addition, we describe an on-line demo for the index with real Web data.\",\"PeriodicalId\":20483,\"journal\":{\"name\":\"Proceedings of the 2016 International Conference on Management of Data\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2016 International Conference on Management of Data\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2882903.2914838\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2016 International Conference on Management of Data","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2882903.2914838","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this extended abstract, we explore the use of MinHash Locality Sensitive Hashing (MinHash LSH) to address the problem of indexing and searching Web data. We discuss a statistical tuning strategy of MinHash LSH, and experimentally evaluate the accuracy and performance, compared with inverted index. In addition, we describe an on-line demo for the index with real Web data.
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