CAREER: FIREFLY - Rich Explanations for Database Queries

CAREER: FIREFLY - 数据库查询的丰富解释

• 批准号: 1552538
• 负责人: Sudeepa Roy
• 金额: $ 55万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1552538&HistoricalAwards=false
• 关键词: CAREER; FIREFLY; Rich; Explanations; Database

With the recent popularity of Big Data, a range of people including data analysts, scientists, decision makers, and ordinary Internet users are increasingly seeking high level explanations for trends and anomalies in available datasets. Such a user typically runs queries on the datasets, computes aggregates, plots the answers on a graph, and looks for explanations for what she observes. For example, she may ask: "Why are two graphs similar or different?", "Why is a sequence of points increasing or decreasing?", "Why is there a sudden spike or dip in a graph?", and so on. Existing data analysis systems focus on large-scale statistical analytics, multi-dimensional data aggregation, interactive data exploration, and sophisticated visualization support. However, there are no tools currently available that offer semantic explanations to users. This project develops a toolkit named FIREFLY (Formal Interactive Rich Explanations On-The-Fly) that provides fast, rich, insightful explanations in response to such 'why' questions asked by users. The automatic explanations provided by this tool will help users harness Big Data more effectively, and the research findings of the project will enrich Big Data analytics techniques. Furthermore, the courses developed in conjunction with this project and the research experience that it will provide students at various levels will help train them to be future researchers. Special attention will be paid to supporting diversity in this process. This project introduces a new perspective in data analysis principled upon the notions of causality, counterfactuals, and interventions. FIREFLY aims to find synopses of properties on input tuples as explanations, such that by restricting the database to tuples that entail a different value of these synopses, the answer to the query and the observation of the user changes, thereby explaining the observation. In order to efficiently return meaningful synopses as explanations, this project will develop theory, algorithms, and optimizations along three main research directions: (1) a rich framework will be established to support meaningful explanations, large classes of database queries, and a variety of questions asked by the users, (2) an interactive tool with a graphical user interface will be built to help users run queries, ask questions, and explore the explanations returned by the tool, and (3) new techniques will be developed to handle uncertainty in the input data and in the explanations themselves.

随着大数据的最新流行，包括数据分析师，科学家，决策者和普通互联网用户在内的许多人越来越多地寻求有关可用数据集中趋势和异常的高水平解释。这样的用户通常在数据集上运行查询，计算聚合，绘制答案在图表上，并为她观察到的内容寻找解释。例如，她可能会问：“为什么两个图相似或不同？”，“为什么一系列点会增加或减少？”，“为什么图中有突然的尖峰或蘸酱？”，等等。现有的数据分析系统着重于大规模统计分析，多维数据聚合，交互式数据探索和复杂的可视化支持。但是，目前尚无工具为用户提供语义解释。该项目开发了一个名为Firefly的工具包（即时正式的互动丰富的解释），该工具包为用户提出的“为什么”问题提供了快速，丰富，有见地的解释。该工具提供的自动解释将帮助用户更有效地利用大数据，该项目的研究结果将丰富大数据分析技术。此外，这些课程与该项目结合开发，并且将为各个层面的学生提供的研究经验将有助于培训他们成为未来的研究人员。在此过程中支持多样性将特别注意。该项目介绍了有关因果关系，反事实和干预概念的数据分析的新观点。 Firefly的目的是在输入元素上找到属性的概要作为解释，以便通过将数据库限制在需要这些概述的不同值的元组中，对查询的答案和对用户的观察的变化，从而解释了观察。 In order to efficiently return meaningful synopses as explanations, this project will develop theory, algorithms, and optimizations along three main research directions: (1) a rich framework will be established to support meaningful explanations, large classes of database queries, and a variety of questions asked by the users, (2) an interactive tool with a graphical user interface will be built to help users run queries, ask questions, and explore the explanations returned by the工具和（3）将开发新技术来处理输入数据和解释本身中的不确定性。

项目成果

iQCAR: A Demonstration of an Inter-Query Contention Analyzer for Cluster Computing Frameworks

• DOI: 10.1145/3183713.3193567
• 发表时间: 2018-01-01
• 期刊: SIGMOD'18: PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA
• 作者: Kalmegh, Prajakta;Lundberg, Harrison;Roy, Sudeepa
• 通讯作者: Roy, Sudeepa