Effective Information Retrieval for Partitioned Document Collections

分区文档集合的有效信息检索

• 批准号: DP0343724
• 负责人: Prof Alistair Moffat
• 金额: $ 4.57万
• 依托单位: The University of Melbourne
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2003
• 资助国家: 澳大利亚
• 起止时间: 2003-01-01 至 2007-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP0343724
• 关键词: Effective; Information; Retrieval; Partitioned; Document

Current information retrieval services make use of massive indexes in order to resolve content-based queries. Monolithic approaches like this have been effective until now because the volume of data stored has been manageable on a single machine or tightly-coupled cluster of machines, and because the data has been available for collection. But with an increasing amount of automatically generated data, and an increasing diversity of information sources, other approaches are required. In this project we will investigate mechanisms for handling retrieval tasks when the indexes to the data are stored locally with the data, and when no central index is viable.

当前的信息检索服务使用海量索引来解决基于内容的查询。到目前为止，像这样的单一方法一直很有效，因为存储的数据量在一台机器或紧密耦合的机器集群上是可管理的，而且数据已经可以收集。但随着自动生成的数据量不断增加，以及信息源的日益多样化，还需要其他方法。在本项目中，我们将研究当数据的索引与数据一起存储在本地并且没有中央索引可行时处理检索任务的机制。