Effective Information Retrieval for Partitioned Document Collections

分区文档集合的有效信息检索。当前的信息检索服务使用大量索引来解析基于内容的查询。到目前为止，像这样的整体方法一直是有效的，因为存储的数据量可以在单个机器或紧密耦合的机器集群上进行管理，并且可以收集数据。但是，随着自动生成的数据量的增加和信息源的日益多样化，需要其他方法。在这个项目中，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心索引时处理检索任务的机制。

Effective Information Retrieval for Partitioned Document Collections. 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.