III: Medium: Collaborative Research: From Answering Questions to Questioning Answers (and Questions)---Perturbation Analysis of Database Queries

III：媒介：协作研究：从回答问题到质疑答案（和问题）——数据库查询的扰动分析

• 批准号: 1408846
• 负责人: Jun Yang
• 金额: $ 88.83万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1408846&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; Answering

In the age of data ubiquity, decision making is increasingly driven by data. Oftentimes, database queries are used to identify issues, debate trategies, make choices, and explain decisions. How these database queries are formulated can significantly influence the decision making process. A poor choice of query parameters---be it intentionally or accidentally---may give a biased view of the underlying data, and lead to decisions that are wrong, misguided, or "brittle" when reality deviates from assumptions. Database research has in the past focused on how to answer queries, but has not devoted much attention to how queries impact decision making, or how to formulate "good" queries from the outset. This project aims to fill this void. The key insight is perturbation analysis of database queries---i.e., studying how perturbations of the query form and parameters affect the query result. For example, slight query perturbations leading to very different results help identify potential pitfalls in decision making. In general, perturbation analysis reveals how queries affect the robustness and objectivity of decisions, and helps decision makers identify "good" queries that will influence their decisions.This project plans to carry out a systematic study of perturbation analysis of database queries. On the modeling front, the project proposes query response surface (QRS) over the parametric space as a framework for perturbation analysis. Intuitive notions of query "goodness" (for the purpose of supporting decisions), such as fairness and robustness, can be formulated as statistical, geometric, and topological properties of the QRS. The framework also allows practical problems to be formulated in terms of the QRS. For example, a brittle decision can be illustrated by identifying its pitfalls, which can be cast as an optimization problem of searching the QRS for slight perturbations with large result deviations; the problem of finding "good" queries that will influence a decision can be cast as that of finding points with desired properties in the relevant region of the QRS. On the algorithmic front, fundamental research problems arise in coping with the complexity of QRS and the vast space of perturbations. While there has been much study on perturbations of data, considering perturbations of queries poses novel challenges and compounds existing ones. The project will develop both efficient representations of QRS and fast algorithms for exploring and analyzing the QRS, using scalable techniques for indexing, optimization, and incremental evaluation that rely on sampling, approximation, and geometric insights. On the systems and applications front, this project plans to deliver the core features of perturbation analysis as a web service with a public API, and address the design and scalability challenges. The project will produce a general-purpose website for applying perturbation analysis of database queries, as well as websites customized for several domains of public interest. The websites will include a facet-driven interface and features that help collaboration and dissemination. In today's data-driven society, there is increasing demand for the proposed research in many application domains such as public policy, urban planning, business intelligence, and health care This project will significantly expand the functionality of database systems, making them easier to use (and harder to misuse) for a new generation of data-driven decision makers, especially those outside the traditional "data-heavy" disciplines such as computer science and statistics. This project will develop courses, seminars, and workshops targeting this much broader population of data-driven decision makers, to help train them in data and quantitative analysis, and in interpreting results critically.For further information see the project web site at: http://db.cs.duke.edu/projects/pq

在数据无处不在的时代，决策越来越多地由数据驱动。通常，数据库查询用于识别问题，辩论策略，做出选择和解释决策。如何制定这些数据库查询可以显着影响决策过程。一个糟糕的查询参数选择-无论是有意还是无意-都可能对底层数据产生偏见，并在现实偏离假设时导致错误的、误导的或“脆弱的”决策。过去的数据库研究集中在如何回答查询，但没有投入太多的关注查询如何影响决策，或者如何从一开始就制定“好”的查询。该项目旨在填补这一空白。关键的洞察力是数据库查询的扰动分析-即，研究查询形式和参数的扰动如何影响查询结果。例如，轻微的查询干扰导致非常不同的结果有助于识别决策中的潜在陷阱。一般来说，扰动分析揭示查询如何影响决策的稳健性和客观性，并帮助决策者识别将影响其决策的“好”查询。本项目计划对数据库查询的扰动分析进行系统研究。在建模方面，该项目提出了参数空间上的查询响应面（QRS）作为扰动分析的框架。查询“良好性”的直观概念（出于支持决策的目的），例如公平性和鲁棒性，可以用公式表示为QRS的统计、几何和拓扑性质。该框架还允许在QRS方面制定实际问题。例如，可以通过识别其陷阱来说明脆弱的决策，其可以被转换为搜索具有大结果偏差的轻微扰动的QRS的优化问题;找到将影响决策的“好”查询的问题可以被转换为在QRS的相关区域中找到具有期望属性的点的问题。在算法方面，基础研究问题出现在应对QRS波群的复杂性和巨大的扰动空间。虽然已经有很多关于数据扰动的研究，但考虑查询的扰动带来了新的挑战并使现有的挑战复杂化。该项目将开发QRS波群的有效表示和快速算法，用于探索和分析QRS波群，使用可扩展技术进行索引，优化和增量评估，这些技术依赖于采样，近似和几何见解。在系统和应用程序方面，该项目计划将扰动分析的核心功能作为具有公共API的Web服务提供，并解决设计和可扩展性方面的挑战。该项目将建立一个通用网站，对数据库查询进行扰动分析，并为若干公众感兴趣的领域定制网站。这些网站将包括一个分面驱动的界面和有助于协作和传播的功能。在当今数据驱动的社会中，在公共政策、城市规划、商业智能和医疗保健等许多应用领域，对拟议研究的需求越来越大。该项目将大大扩展数据库系统的功能，使其更易于使用（也更难滥用），为新一代数据驱动的决策者，特别是那些传统的“数据密集”学科以外的学科，如计算机科学和统计学。该项目将针对更广泛的数据驱动决策者群体开发课程、研讨会和讲习班，以帮助培训他们进行数据和定量分析，并对结果进行批判性解释。http://db.cs.duke.edu/projects/pq