III: Small: In-memory, Distributed, and Adaptive Spatio-textual Query Processing

具有gps功能的智能手机的广泛使用，以及微博和社交网络（如Twitter和Facebook）的普及，导致了大量文本数据的产生。通常情况下，这些文本数据（例如tweets）是根据文本数据产生的位置进行地理标记的。许多应用程序很好地利用了这种地理标记文本数据流（也称为空间关键字或空间文本数据），并基于数据的文本和空间组件向用户提供服务。应用程序需要处理大量针对空间文本数据的用户查询。例如，位置感知广告目标发布/订阅系统需要根据用户的位置和文本配置文件向数百万用户传播数百万个广告和促销活动。该项目将解决这些应用程序及其底层系统面临的障碍，以便正常运行。更具体地说，该项目将解决空间文本服务器面临的以下研究挑战：(1)实时支持大量空间文本数据流和查询的可扩展性挑战；(2)表达能力的挑战，体现在缺乏充分表达复杂空间文本查询的机制——查询能力需要与不断发展的位置服务的日益复杂和复杂相匹配；(3)适应性挑战，系统需要适应数据分布随时间的变化，因为在位置服务中，位置数据分布、用户兴趣和热门关键词主题会随着时间的变化而变化，而可扩展的空间文本服务器需要不断适应这些变化。该项目将在处理对连续流空间文本数据的大量查询时解决这些可扩展性、表现力和适应性挑战。该项目将研究如何在内存分布式数据系统中支持空间文本数据和查询作为一级公民。将研究用于处理大量空间文本数据和连续查询的可扩展架构。与定制的解决方案相比，将开发类似关系的空间文本构建块操作符来表达扩展的sql空间文本查询，以及成本计算、代数转换规则和查询优化技术。为了解决可扩展性和工作负载的变化，将开发自适应和频率感知的内存分布式索引和查询处理技术，以动态组织和处理不断发展的空间文本数据。所要开发的空间文本索引和查询处理技术将动态地考虑不同空间区域内关键词频率的变化和差异，从而自动选择优化系统性能的最佳空间文本数据组织。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The widespread use of GPS-enabled smartphones along with the popularity of microblogging and social networking, e.g., Twitter and Facebook, has resulted in producing large amounts of text data. Typically, this text data, e.g., the tweets, are geo-tagged by the location in which the text data has been produced. Many applications make good use of this stream of geo-tagged text data (also termed spatial-keyword or spatio-textual data), and provide services to users based on the textual and the spatial components of the data. Applications need to process large numbers of user queries against spatio-textual data. For example, location-aware ad targeting publish/subscribe systems are required to disseminate millions of ads and promotions to millions of users based on the users' locations and textual profiles. This project will address the hurdles that face these applications and their underlying systems in order to function properly. More specifically, this project will address the following research challenges that face spatio-textual servers: (1) the scalability challenge to support large amounts of spatio-textual data streams and queries in real-time; (2) the expressiveness challenge that is exemplified in the lack of mechanisms that adequately express complex spatio-textual queries -- querying capabilities need to match the growing sophistication and complexity of the continuously evolving location services; and (3) the adaptivity challenge, where systems need to adapt to changes in data distribution over time because in location services, location-data distribution, users' interests, and hot keyword topics change over time and a scalable spatio-textual server needs to continuously adapt to these changes.The project will address these scalability, expressiveness, and adaptivity challenges when processing large numbers of queries on continuously-streamed spatio-textual data. The project will investigate how to support spatio-textual data and queries as first-class citizens in an in-memory distributed data system. Scalable architectures for handling large amounts of spatio-textual data and continuous queries will be investigated. In contrast to tailored solutions, relational-like spatio-textual building-block operators will be developed to express extended-SQL spatio-textual queries along with costing, algebraic transformation rules, and query optimization techniques. To address scalability and the variation in the workload, adaptive and frequency-aware in-memory distributed indexing and query processing techniques will be developed to dynamically organize and process the continuously-evolving spatio-textual data. The spatio-textual indexing and query processing techniques to be developed will dynamically account for the changes and differences in the frequencies of keywords within the various spatial regions to automatically choose the best spatio-textual data organization that optimizes the system performance.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.