Modeling Time, Space, and Networks for Effective and Efficient Information Retrieval

现代社会的公民被越来越多的信息淹没，看不到缓解的迹象。搜索仍然是今天对抗这种洪水的最佳解决方案之一。在当今的移动的世界中，搜索系统需要了解时间、空间和网络，因为它们与用户需求有关：例如，我们需要最新的事实，更喜欢附近的位置，并向朋友寻求帮助。类似地，搜索系统需要对上下文的这些重要方面进行建模：文档是在特定的时间和位置创建的，通常与其他文档有明确的关系（例如，网络上的超链接，回复推文）。**我的研究项目位于信息检索和大数据的交叉点，旨在构建工具，帮助用户在大型文本集合中找到相关信息，同时应对计算机和信息科学中的基本挑战。我的重点是探索时间，空间和网络之间的丰富交互作用，以构建既有效又有效的搜索系统（即，返回高质量结果）和高效（即，表现出低查询延迟、小存储器占用等）。我提出了一种集成的方法，将有助于高效和可扩展的索引，用于查询具有时间，空间和网络元数据的文档;丰富的上下文模型，用于文档排名和预测用户建模;以及支持真实世界信息搜索的跨平台接口。这些能力将在两个主要应用中展示：（1）社交媒体流上的实时信息过滤-记者可能对“跟踪”社交媒体帖子流以获得关于特定主题的更新感兴趣，例如，欧洲难民危机。(2)网络档案的学术分析-网络档案包含网络内容的时间快照，并为研究最近过去的数字历史学家或探索政治辩论如何演变的社会科学家提供宝贵的资源。这项研究计划的影响是多方面的：更好地了解时间，空间和网络在搜索中的作用将为未来复杂任务的搜索引擎设计提供信息。凭借在Twitter构建生产“大数据”分析基础设施和面向用户的产品方面的丰富经验，我在弥合学术界和工业界之间的鸿沟方面有着良好的记录;这些经验将有助于成功的技术转让。我以前的学生和博士后研究人员拥有非常令人垂涎的技能，知识和大数据工作经验，我的研究计划将继续为加拿大和世界各地的现代信息中心经济培养有价值的贡献者。**

Citizens of modern societies are inundated with increasing quantities of information with no relief in sight. Search remains one of the best solutions today for combating this deluge. In today's mobile world, search systems need to understand time, space, and networks as they pertain to user needs: for example, we demand the most up-to-date facts, prefer nearby locations, and ask friends for help. Similarly, search systems need to model these important aspects of context: documents are created at specific times and locations, often with well-defined relationships to other documents (e.g., hyperlinks on the web, reply to a tweet).******My research program lies at the intersection of information retrieval and big data, and aims to build tools that help users find relevant information in large text collections while tackling fundamental challenges in computer and information science. My focus is to explore the rich interactions between time, space, and networks in building search systems that are both effective (i.e., return high quality results) and efficient (i.e., exhibit low query latencies, small memory footprints, etc.). I propose an integrated approach that will make contributions to efficient and scalable indexes for querying documents that have temporal, spatial, and network metadata; rich contextual models of relevance for document ranking and predictive user modeling; and cross-platform interfaces that support real-world information seeking.******These capabilities will be demonstrated in two main applications: (1) Real-time information filtering on social media streams - a journalist might be interested in "tracking" a stream of social media posts for updates about a particular topic, e.g., the European refugee crisis. (2) Scholarly analysis of web archives - web archives contain temporal snapshots of web content and offer valuable resources for digital historians studying the recent past or social scientists exploring how political debates evolve.******The impact of this research program is many-fold: A better understanding of the roles that time, space, and networks play in search will inform the design of future search engines for complex tasks. With substantial previous experience at Twitter building production "big data" analytics infrastructure and user-facing products, I have a strong record of bridging the divide between academia and industry; these experiences will assist in successful technology transfer. My former students and post-doctoral researchers possess highly-coveted skills, knowledge, and experience in working with big data, and my research program will continue training valuable contributors to modern information-centric economies in Canada and around the world.*** **