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，随着不同媒体上信息量的增长，我们被越来越多的信息淹没，信息的速度也在增长--信息生成、传输和消费的速度。Twitter和博客等社交媒体的日益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推进信息检索研究的最新水平。这项工作包括两个主题：第一个涉及高性能的搜索架构，低延迟，高吞吐量的查询评估和索引;第二个涉及相关性算法，探索策略来模拟时变相关性信号的学习排名框架。增强的实时搜索能力有望使用户更有效地获取时间敏感的信息。场景包括记者跟踪地球仪各地的情况，自然灾害的受害者试图找到亲人，政治分析家消化对候选人演讲的反应。该项目预计将产生一个开放源码的演示平台，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内的评估密切协调，进一步使更广泛的研究社区受益。更多的信息将通过项目网站（http：//www.example.com）传播。www.umiacs.umd.edu/~jimmylin/projects/研究成果将被纳入大数据计算课程的课堂材料，将云计算带入课堂，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.