III: Small: Scalable Event Trend Analytics For Data Stream Inquiry

近年来，数据流以前所未有的规模和速度增长。实时发现数据流中新出现的事件趋势对于从计算跨主要医疗设施的感染传播模式到检测频繁的库存趋势等时间关键型应用程序至关重要。这个项目克服了最先进的系统的缺点，并为这类重要的分析提供了实用的解决方案。提供这些事件趋势分析服务的变革性策略的发明正在改变游戏规则。影响我们日常生活的事件流应用程序包括医疗保健和金融欺诈。该项目活动与未来STEM劳动力培训的整合将在数据科学的一个新的跨学科学位课程中进行。该项目开发了良好的理论基础和实际处理策略，使应用程序能够追求具有高响应性的交互式事件趋势分析。该项目在现代流处理系统中实现了基于kleene闭包的分析。可扩展的事件趋势分析允许应用程序从高速流数据中获得与趋势相关的见解。项目活动包括：(a)表达性事件趋势建模和匹配语义，(b)紧凑的事件趋势编码技术，(c)聚合下推到Kleene闭包计算的优化方法，避免昂贵的趋势构建，(d)多个粒度级别的聚合计算，(e)查询处理范式的正确性和完整性的原则基础，以及(f)分布式和共享执行趋势分析以扩大规模。数据库系统中的原则，如查询重写、优化、执行共享和可伸缩云计算，都应用于此问题。创新包括有选择地跳过昂贵趋势构建的趋势聚合策略，以及聚合状态模型的压缩方法。将与医疗保健和金融应用程序中的行业协作者合作，使用实际工作负载验证这些方法的实用性和有效性。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。

Data streams have grown in unprecedented scale and velocity in recent years. The real-time discovery of emerging event trends in data streams is essential for time-critical applications from computing infection spread patterns across major medical facilities to detecting frequent stock trends. This project overcomes the shortcomings of state-of-the-art systems and provides practical solutions for this important class of analytics. The invention of transformative strategies to provide these Event Trend Analytics services is game changing. Event stream applications affecting our daily lives include health care and financial fraud. The integration of this project's activities with the training of a future STEM workforce will occur within a new interdisciplinary degree program in Data Science. This project develops both a sound theoretical foundation and practical processing strategies that empower applications to pursue interactive event trend analysis with high responsiveness. This project enables Kleene-closure based analytics in modern stream processing systems. Scalable Event Trend Analytics allows applications to gain trend-related insights from high-velocity stream data. Project activities include:(a) expressive event trend modeling and matching semantics, (b) compact event trend encoding techniques, (c) optimizaton methodology for aggregation push-down into the Kleene closure computation avoiding expensive trend construction, (d) aggregation computation at multiple granularity levels, (e) principled foundation of correctness and completeness of the query processing paradigm, and (f) distributed and shared execution of trend analytics for scale-up. Principles from database systems, such as query rewriting, optimization, execution sharing, and scalable cloud computing, are applied to this problem. Innovations include trend aggregation strategies that selectively skip expensive trend construction, and compression methods for aggregation state models. These methods will be validated for utility and effectiveness using real-world workloads in partnerships with industry collaborators in healthcare and financial applications.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.