DC: Small: An Integrated Architecture for Federated Search

DC：小型：联合搜索的集成架构

• 批准号: 0916553
• 负责人: Jamie Callan
• 金额: $ 49.97万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916553&HistoricalAwards=false
• 关键词: DC; Small; Integrated; Architecture; Federated

This project is developing a general solution to two new federated search problems. Massive web datasets are made more manageable by dividing them into topic-oriented "shards," and then searching only a few shards per query, thus reducing computational costs dramatically. Integration of focused "vertical search services" into general-purpose search portals is made more manageable by a framework that uses multiple techniques to characterize the contents of each resource and track how its content and query traffic change over time. Introducing resource definition policies and diverse information into federated search requires solving a variety of new resource representation, resource selection, and result merging problems. This research is also addressing the requirements of dynamic resources, and looks beyond average case analysis to characterize the range of accuracy that a federated search service experiences. Reducing the computational costs of searching massive web corpora enables greater academic study of large web datasets, and lowers costs for web search companies. A comprehensive framework for integrating specialized information services in web portals makes it easier for commercial web search portals to deploy new search services.New algorithms are being disseminated in the open-source Lemur Toolkit, thus making it very accessible. Datasets are being published in a form that enables them to be recreated precisely or closely by other researchers. Queries and relevance judgments are being published so that they may be used by other researchers. This project is an extension of research done in IIS-0841275, SGER: Multi-Tier Indexing for Web Search Engines.Project URL: http://www.cs.cmu.edu/~callan/Projects/IIS-0916553/

这个项目正在开发两个新的联邦搜索问题的通用解决方案。通过将大量web数据集划分为面向主题的“碎片”，然后每次查询只搜索几个碎片，从而大大降低了计算成本，从而使它们更易于管理。通过使用多种技术来描述每个资源的内容并跟踪其内容和查询流量如何随时间变化的框架，将重点突出的“垂直搜索服务”集成到通用搜索门户中变得更易于管理。在联邦搜索中引入资源定义策略和各种信息需要解决各种新资源表示、资源选择和结果合并问题。这项研究还解决了动态资源的需求，并超越了一般的案例分析来描述联邦搜索服务所经历的准确性范围。减少搜索大量网络语料库的计算成本，可以对大型网络数据集进行更大的学术研究，并降低网络搜索公司的成本。用于在web门户中集成专门信息服务的综合框架使商业web搜索门户更容易部署新的搜索服务。新的算法正在开源的Lemur工具包中传播，从而使其非常容易访问。数据集正在以一种能够被其他研究人员精确或紧密地重建的形式发布。查询和相关性判断正在被发表，以便其他研究人员可以使用。本项目是isis -0841275， SGER: Web搜索引擎的多层索引研究的延伸。项目网址：http://www.cs.cmu.edu/~callan/Projects/IIS-0916553/