CRII: CHS: Modeling Analysis Behavior to Support Interactive Exploration of Massive Datasets

科学家通常使用探索性数据分析方法来从数据中获得见解。 然而，数据源数量和粒度的增加引发了规模问题，使开发工具这一本来就很困难的问题变得更加复杂，这些工具可以帮助分析师管理其探索性工作所建议的经常变化的目标和分析轨迹。 该项目重点改进探索性数据分析工具中的两个关键系统：提供数据图形表示的可视化系统，以及在后端高效管理大规模数据以支持分析的数据管理系统。 关键思想是集成这两个系统，首先根据分析师在可视化系统中的最近操作推断他们的目标和未来行为，然后使用这些信息在数据管理系统中主动构建高效的处理查询。 这样做可以缩短系统响应时间，从而提高分析师使用系统的能力以及他们获得的见解；开发的技术将为数据库、可视化和人机交互社区做出贡献。 这些工具本身将使许多科学和工业领域受益，该团队还将利用该项目工作来支持新的跨学科数据科学课程，以及为计算机科学领域代表性不足的学生提供外展和研究机会。为了提高性能，该项目将为视觉探索系统制定动态优化策略，随着时间的推移推断用户的探索性分析目标，并部署适合当前分析目标的优化算法。 这些优化将解决人类绩效（即科学家或分析师如何有效地利用视觉探索系统提取见解并执行分析任务）和系统性能（即系统如何有效地响应用户的交互）。这些优化的开发将分两个阶段进行。首先，将进行用户研究，以表征在不同的探索性数据分析场景和系统设计下用户如何与视觉探索系统交互。其次，使用收集到的研究数据，将设计预测查询执行引擎，从日志数据推断用户的分析目标，并检测行为随时间的变化。为了提高数据管理系统性能，现有技术将适应利用预测查询执行引擎，包括可能即将到来的查询的查询调度和多查询优化，以利用最近查询和预测查询之间的计算重叠。为了提高可视化系统和人类绩效，系统将向分析师推荐预测的下一个查询，而项目团队将进行性能驱动的界面设计工作，以根据预测查询执行引擎收集的数据设计新的交互。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

Scientists commonly use exploratory data analysis methods to gain insights from their data. However, increases in the number and granularity of data sources raise problems of scale that complicate the already difficult problem of developing tools that help analysts manage the often-changing goals and analysis trajectories suggested by their exploratory work. This project focuses on improving two key systems in exploratory data analysis tools: the visualization systems that provide graphical representations of the data, and the data management systems that efficiently manage large-scale data on the back end to support the analysis. The key idea is to integrate these two systems by first inferring analysts' goals and future behaviors from their recent actions in the visualization system, then using those to proactively construct efficient processing queries in the data management system. Doing this should improve system response times, which should in turn improve analysts' ability to use the system and the insights they gain; the techniques developed will contribute to the database, visualization, and human-computer interaction communities. The tools themselves stand to benefit a number of scientific and industrial domains, and the team will also use the project work to support new interdisciplinary data science courses along with outreach and research opportunities for underrepresented students in computer science.To improve performance, this project will produce dynamic optimization strategies for visual exploration systems, which infer the user's exploratory analysis goals over time, and deploy optimization algorithms tailored to the current analysis goal. These optimizations will address both human performance, i.e., how effectively a scientist or analyst extracts insights and performs analysis tasks with a visual exploration system, and system performance, i.e., how efficiently and effectively the system responds to a user's interactions. The development of these optimizations will be done in two phases. First, a user study will be conducted to characterize how users interact with visual exploration systems under different exploratory data analysis scenarios and system designs. Second, using the collected study data, a predictive query execution engine will be designed to infer users' analysis goals from log data and detect shifts in behaviors over time. To boost data management system performance, existing techniques will be adapted to leverage the predictive query execution engine, including query scheduling of likely upcoming queries and multi-query optimization to leverage computational overlap between recent and predicted queries. To boost visualization system and human performance, the system will recommend predicted next queries to analysts, while the project team will conduct performance-driven interface design work to design new interactions based on data collected by the predictive query execution engine.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.