III: Small: Providing Relevant and Timely Results: Real-Time Search Architectures and Relevance Algorithms

信息搜索仍然是当今满足个人解决问题需求的最佳解决方案之一。然而，随着信息量在不同媒体上的增长，我们被越来越多的信息淹没，其速度也在增加--信息被产生、传输和消费的速度。Twitter和博客等社交媒体日益增长的重要性进一步加剧了这一问题。显然，需要更好的实时搜索能力。该项目旨在通过解决实时搜索问题来提高信息检索研究的最新水平。这项工作包括两个主题：第一个主题是针对低延迟、高吞吐量查询评估和索引的高性能搜索架构；第二个主题是相关性算法，探索在学习到排名框架中对时变相关性信号进行建模的策略。增强的实时搜索功能承诺为用户提供更有效的访问时间敏感信息的途径。场景包括记者跟踪世界各地的情况，自然灾害的受害者试图寻找亲人，政治分析人士消化对候选人演讲的反应。该项目预计将产生一个开源演示平台，用于在推特和博客上进行实时搜索。在NIST赞助的文本检索会议(TREC)上与共享的、社区范围的评估进行密切协调，进一步使更广泛的研究界受益。将通过项目网站((http://www.umiacs.umd.edu/~jimmylin/projects/)发布更多信息。研究成果将被纳入将云计算带入课堂的大数据计算课程的课堂材料中，研究生将有机会获得研究和系统开发经验。

Information search remains one of the best solutions today for satisfying individuals' problem-solving needs. However, we are inundated with growing quantities of information as its volume on different media grows, and so does its velocity -- the rate at which information is being generated, transmitted, and consumed. The growing importance of social media such as Twitter and blogs further exacerbates this problem. It is clear that better real-time search capabilities are needed. This project aims to advance the state of the art in information retrieval research by tackling the real-time search problem. The effort consists of two themes: the first concerns high-performance search architectures for low-latency, high-throughput query evaluation and indexing; the second concerns relevance algorithms, exploring strategies to model time-varying relevance signals in a learning-to-rank framework. Enhanced real-time search capabilities promise to provide users more effective access to time-sensitive information. Scenarios include journalists tracking situations around the globe, victims of natural disaster trying to find loved ones, and political analysts digesting reactions to a candidate's speech. This project is expected to yield an open-source demonstration platform for real-time search on tweets and blogs. Close coordination with shared, community-wide evaluations at the NIST-sponsored Text Retrieval Conferences (TREC) further benefits the broader research community. More information is will disseminated via the project web site (http://www.umiacs.umd.edu/~jimmylin/projects/ ). Research results will be incorporated into class material for the large-data computing course that brings cloud computing into the classroom, and graduate students will have an opportunity to gain research and system development experience.