Beyond keyword search for ranked document retrieval

这个项目将开发新的方法，使用从自索引派生的一类新的排名感知算法来高效和有效地进行排名文本检索。这些算法可以即时支持复杂的统计计算。随着能源成本持续飙升，高效的大数据算法设计成为一个日益重要的问题，对于谷歌等大数据消费者来说，这一问题现在可能超过硬件成本。在这个项目中，研究了网络搜索中的两个重要问题：实时索引和长格式查询应答。使用自索引算法，该项目提供了一个路线图，超越了简单的基于关键字的排名文档检索，从而使我们能够在未来十年高效地满足用户更苛刻的信息需求。

This project will develop novel approaches to efficient and effective ranked text retrieval using a new class of rank-aware algorithms derived from self-indexes. These algorithms can support complex statistical calculations on the fly. Efficient algorithm design for big data is an increasingly important problem as energy costs continue to soar and can now exceed hardware costs for big data consumers such as Google. In this project, two important problems in web search are explored: real-time indexing and long-form query answering. Using self-index algorithms, this project presents a road map to move beyond simple keyword-based ranked document retrieval, thus allowing us to efficiently meet more demanding information needs of users in the next decade.