高维向量相似搜索:从时间序列到深度网络嵌入

Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data Pub Date : 2020-05-29 DOI:10.1145/3318464.3384402

Karima Echihabi

{"title":"高维向量相似搜索:从时间序列到深度网络嵌入","authors":"Karima Echihabi","doi":"10.1145/3318464.3384402","DOIUrl":null,"url":null,"abstract":"Similarity search is an important and challenging problem that is typically modeled as nearest neighbor search in high dimensional space, where objects are represented as high dimensional vectors and their (dis)similarity is evaluated using a distance measure such as the Euclidean distance.","PeriodicalId":436122,"journal":{"name":"Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data","volume":"398 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"High-Dimensional Vector Similarity Search: From Time Series to Deep Network Embeddings\",\"authors\":\"Karima Echihabi\",\"doi\":\"10.1145/3318464.3384402\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Similarity search is an important and challenging problem that is typically modeled as nearest neighbor search in high dimensional space, where objects are represented as high dimensional vectors and their (dis)similarity is evaluated using a distance measure such as the Euclidean distance.\",\"PeriodicalId\":436122,\"journal\":{\"name\":\"Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data\",\"volume\":\"398 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3318464.3384402\",\"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 2020 ACM SIGMOD International Conference on Management of Data","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3318464.3384402","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 11

摘要

相似度搜索是一个重要且具有挑战性的问题，通常被建模为高维空间中的最近邻搜索，其中对象被表示为高维向量，并且使用距离度量(如欧几里得距离)来评估它们的(非)相似度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

High-Dimensional Vector Similarity Search: From Time Series to Deep Network Embeddings

Similarity search is an important and challenging problem that is typically modeled as nearest neighbor search in high dimensional space, where objects are represented as high dimensional vectors and their (dis)similarity is evaluated using a distance measure such as the Euclidean distance.

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

Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

自引率

0.00%

发文量