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他们的数据中获得见解。然而，数据源数量和粒度的增加带来了规模问题，使本已棘手的问题复杂化，即开发工具，帮助分析人员管理他们的探索性工作所提出的经常变化的目标和分析轨迹。本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。其核心思想是将这两个系统集成在一起，首先从分析人员在可视化系统中的近期行为推断他们的目标和未来行为，然后使用这些行为在数据管理系统中主动构建高效的处理查询。这样做应该会缩短系统响应时间，进而提高分析人员使用系统的能力和他们获得的洞察力；开发的技术将有助于数据库、可视化和人机交互社区。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，以及为计算机科学中代表性不足的学生提供扩展和研究机会。为了提高性能，该项目将为视觉探索系统生成动态优化策略，该策略将推断用户随着时间的推移的探索性分析目标，并部署根据当前分析目标定制的优化算法。这些优化既涉及人的表现，即科学家或分析员如何有效地提取洞察力并使用视觉探索系统执行分析任务，也将涉及系统性能，即系统对用户交互的反应有多高和效率。这些优化的开发将分两个阶段进行。首先，将进行一项用户研究，以确定在不同的探索性数据分析场景和系统设计下，用户如何与可视化探索系统交互。其次，使用收集的研究数据，将设计一个预测性查询执行引擎，从日志数据推断用户的分析目标，并检测随着时间的推移行为的变化。为了提高数据管理系统的性能，将调整现有技术以利用预测性查询执行引擎，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。为了提升可视化系统和人类性能，系统将向分析师推荐预测的NEXT查询，而项目团队将进行性能驱动的界面设计工作，以基于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势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.