{"title":"用于回答时间序列数据查询的多尺度直方图","authors":"Lei Chen, M. Tamer Özsu","doi":"10.1109/ICDE.2004.1320068","DOIUrl":null,"url":null,"abstract":"Similarity-based time series data retrieval has been used in many real world applications, such as stock data or weather data analysis. Two types of queries on time series data are generally studied: pattern existence queries and exact match queries. Here, we describe a technique to answer both pattern existence queries and exact match queries. A typical application that needs answers to both queries is an interactive analysis of time series data. We propose a histogram-based representation to approximate time series data.","PeriodicalId":358862,"journal":{"name":"Proceedings. 20th International Conference on Data Engineering","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Multi-scale histograms for answering queries over time series data\",\"authors\":\"Lei Chen, M. Tamer Özsu\",\"doi\":\"10.1109/ICDE.2004.1320068\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Similarity-based time series data retrieval has been used in many real world applications, such as stock data or weather data analysis. Two types of queries on time series data are generally studied: pattern existence queries and exact match queries. Here, we describe a technique to answer both pattern existence queries and exact match queries. A typical application that needs answers to both queries is an interactive analysis of time series data. We propose a histogram-based representation to approximate time series data.\",\"PeriodicalId\":358862,\"journal\":{\"name\":\"Proceedings. 20th International Conference on Data Engineering\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-03-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. 20th International Conference on Data Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDE.2004.1320068\",\"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. 20th International Conference on Data Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDE.2004.1320068","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multi-scale histograms for answering queries over time series data
Similarity-based time series data retrieval has been used in many real world applications, such as stock data or weather data analysis. Two types of queries on time series data are generally studied: pattern existence queries and exact match queries. Here, we describe a technique to answer both pattern existence queries and exact match queries. A typical application that needs answers to both queries is an interactive analysis of time series data. We propose a histogram-based representation to approximate time series data.