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

CRII：CHS：建模分析行为以支持海量数据集的交互式探索

• 批准号: 1850115
• 负责人: Leilani Battle
• 金额: $ 17.5万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1850115&HistoricalAwards=false
• 关键词: CRII; CHS; Modeling; Analysis; Behavior

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.

科学家通常使用探索性数据分析方法从数据中获得见解。 然而，数据源的数量和粒度的增加引起了规模问题，使开发工具这一本已困难的问题变得复杂，这些工具可以帮助分析师管理他们的探索性工作所建议的经常变化的目标和分析轨迹。 该项目的重点是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，以及在后端有效管理大规模数据以支持分析的数据管理系统。 其关键思想是将这两个系统集成在一起，首先从分析师在可视化系统中的最近行为中推断出他们的目标和未来行为，然后使用这些来主动构建数据管理系统中的高效处理查询。 这样做可以改善系统响应时间，这反过来又可以提高分析人员使用系统的能力和他们获得的见解;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，该团队还将利用该项目的工作来支持新的跨学科数据科学课程，沿着为计算机科学领域代表性不足的学生提供拓展和研究机会。为了提高性能，该项目将为视觉探索系统提供动态优化策略，该策略将随着时间的推移推断用户的探索性分析目标，并部署针对当前分析目标定制的优化算法。 这些优化将解决人的表现，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，以及系统性能，即，系统对用户交互的响应效率和效果如何。这些优化的开发将分两个阶段进行。首先，将进行用户研究，以表征用户如何在不同的探索性数据分析场景和系统设计下与视觉探索系统交互。其次，使用收集的学习数据，预测查询执行引擎将被设计为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性能，现有技术将适于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的表现，该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，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价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Debugging Database Queries: A Survey of Tools, Techniques, and Users

调试数据库查询：工具、技术和用户调查

• DOI: 10.1145/3313831.3376485
• 发表时间: 2020
• 期刊: CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
• 作者: Gathani, Sneha;Lim, Peter;Battle, Leilani
• 通讯作者: Battle, Leilani

Database Benchmarking for Supporting Real-Time Interactive Querying of Large Data

• DOI: 10.1145/3318464.3389732
• 发表时间: 2020-05
• 期刊: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
• 作者: L. Battle;P. Eichmann;M. Angelini;T. Catarci;G. Santucci;Yukun Zheng;Carsten Binnig;Jean-Daniel Fekete;Dominik Moritz

Vis Ex Machina: An Analysis of Trust in Human versus Algorithmically Generated Visualization Recommendations

• DOI: 10.1145/3411764.3445195
• 发表时间: 2021-01
• 期刊: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
• 作者: Rachael Zehrung;A. Singhal;M. Correll;L. Battle

The Role of Latency and Task Complexity in Predicting Visual Search Behavior

• DOI: 10.1109/tvcg.2019.2934556
• 发表时间: 2020-01-01
• 期刊: IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
• 影响因子: 5.2
• 作者: Battle, Leilani;Crouser, R. Jordan;Stonebraker, Michael
• 通讯作者: Stonebraker, Michael

A Provenance Task Abstraction Framework

起源任务抽象框架

• DOI: 10.1109/mcg.2019.2945720
• 发表时间: 2019
• 期刊: IEEE Computer Graphics and Applications
• 影响因子: 1.8
• 作者: Bors, Christian;Wenskovitch, John;Dowling, Michelle;Attfield, Simon;Battle, Leilani;Endert, Alex;Kulyk, Olga;Laramee, Robert S.
• 通讯作者: Laramee, Robert S.