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，决策越来越受到数据的驱动。通常，数据库查询用于识别问题、讨论策略、做出选择和解释决策。这些数据库查询的表达方式会对决策过程产生重大影响。查询参数的错误选择-无论是有意还是无意-可能会对底层数据产生偏见，并导致在现实与假设背离时做出错误、误导或“脆弱”的决定。过去，数据库研究的重点是如何回答查询，但没有太多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，轻微的查询扰动会导致非常不同的结果，这有助于识别决策过程中的潜在陷阱。总的来说，扰动分析揭示了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曲面(QRS)作为扰动分析的框架。查询“好”的直观概念(用于支持决策的目的)，例如公平性和健壮性，可以被表示为QRS的统计、几何和拓扑属性。该框架还允许在QRS方面制定实际问题。例如，一个脆弱的决策可以通过识别其缺陷来说明，这可以归结为在QRS中搜索具有较大结果偏差的轻微扰动的优化问题；找到将影响决策的“好”查询的问题可以归结为在QRS的相关区域中找到具有所需属性的点的问题。在算法方面，在处理QRS的复杂性和巨大的扰动空间时，出现了基础研究问题。虽然已经有很多关于数据扰动的研究，但考虑查询的扰动提出了新的挑战，并使现有的挑战复杂化。该项目将开发QRS的有效表示和用于探索和分析QRS的快速算法，使用依赖于采样、近似和几何洞察的可扩展索引、优化和增量评估技术。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，并解决设计和可伸缩性方面的挑战。该项目将制作一个用于对数据库查询进行扰动分析的通用网站，以及为几个公共利益领域定制的网站。这些网站将包括一个以面为导向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，对公共政策、城市规划、商业智能和医疗保健等许多应用领域的拟议研究的需求越来越大。该项目将显著扩展数据库系统的功能，使其更容易被新一代数据驱动的决策者使用(也更难被误用)，特别是那些传统的数据密集型学科之外的决策者，如计算机科学和统计学。这个项目将针对更广泛的数据驱动型决策者群体开发课程、研讨会和研讨会，帮助他们在数据和定量分析以及关键解释结果方面进行培训。有关更多信息，请参阅项目网站：http://db.cs.duke.edu/projects/pq