Effective Information Retrieval for Partitioned Document Collections

有效的信息检索分区的文件集。当前的信息检索服务使用大量的索引来解决基于内容的查询。像这样的单片方法到目前为止一直很有效，因为存储的数据量在单个机器或紧密耦合的机器集群上是可管理的，并且因为数据可用于收集。但是随着自动生成的数据量的增加和信息源的日益多样性，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时，以及当没有中央索引可行时，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.