{"title":"数据库查询重构的原则优化框架","authors":"Gautam Das","doi":"10.1145/2795218.2795227","DOIUrl":null,"url":null,"abstract":"Traditional databases have traditionally supported the Boolean retrieval model, where a query returns all tuples that match the selection conditions specified -- no more and no less. Such a query model is often inconvenient for naive users conducting searches that are often exploratory in nature, since the user may not have a complete idea, or a firm opinion of what she may be looking for. This is especially relevant in the context of the Deep Web, which offers a plethora of searchable data sources such as electronic products, transportation choices, apparel, investment options, etc. Users often encounter two types of problems: (a) they may under-specify the items of interest, and find too many items satisfying the given conditions (the many answers problem), or (b) they may over-specify the items of interest, and find no item in the source satisfying all the provided conditions (the empty answer problem). In this talk, I discuss our recent efforts in developing techniques for iterative \"query reformulation\" by which the system guides the user in a systematic way through several small steps, where each step suggests slight query modifications, until the query reaches a form that generates desirable answers. Our proposed approaches for suggesting query reformulations are driven by novel probabilistic frameworks based on optimizing a wide variety of application-dependent objective functions.","PeriodicalId":211132,"journal":{"name":"Proceedings of the Second International Workshop on Exploratory Search in Databases and the Web","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2015-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Principled Optimization Frameworks for Query Reformulation of Database Queries\",\"authors\":\"Gautam Das\",\"doi\":\"10.1145/2795218.2795227\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Traditional databases have traditionally supported the Boolean retrieval model, where a query returns all tuples that match the selection conditions specified -- no more and no less. Such a query model is often inconvenient for naive users conducting searches that are often exploratory in nature, since the user may not have a complete idea, or a firm opinion of what she may be looking for. This is especially relevant in the context of the Deep Web, which offers a plethora of searchable data sources such as electronic products, transportation choices, apparel, investment options, etc. Users often encounter two types of problems: (a) they may under-specify the items of interest, and find too many items satisfying the given conditions (the many answers problem), or (b) they may over-specify the items of interest, and find no item in the source satisfying all the provided conditions (the empty answer problem). In this talk, I discuss our recent efforts in developing techniques for iterative \\\"query reformulation\\\" by which the system guides the user in a systematic way through several small steps, where each step suggests slight query modifications, until the query reaches a form that generates desirable answers. Our proposed approaches for suggesting query reformulations are driven by novel probabilistic frameworks based on optimizing a wide variety of application-dependent objective functions.\",\"PeriodicalId\":211132,\"journal\":{\"name\":\"Proceedings of the Second International Workshop on Exploratory Search in Databases and the Web\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Second International Workshop on Exploratory Search in Databases and the Web\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2795218.2795227\",\"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 Second International Workshop on Exploratory Search in Databases and the Web","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2795218.2795227","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Principled Optimization Frameworks for Query Reformulation of Database Queries
Traditional databases have traditionally supported the Boolean retrieval model, where a query returns all tuples that match the selection conditions specified -- no more and no less. Such a query model is often inconvenient for naive users conducting searches that are often exploratory in nature, since the user may not have a complete idea, or a firm opinion of what she may be looking for. This is especially relevant in the context of the Deep Web, which offers a plethora of searchable data sources such as electronic products, transportation choices, apparel, investment options, etc. Users often encounter two types of problems: (a) they may under-specify the items of interest, and find too many items satisfying the given conditions (the many answers problem), or (b) they may over-specify the items of interest, and find no item in the source satisfying all the provided conditions (the empty answer problem). In this talk, I discuss our recent efforts in developing techniques for iterative "query reformulation" by which the system guides the user in a systematic way through several small steps, where each step suggests slight query modifications, until the query reaches a form that generates desirable answers. Our proposed approaches for suggesting query reformulations are driven by novel probabilistic frameworks based on optimizing a wide variety of application-dependent objective functions.