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III: Small: Automatic Detection and Resolution of Anti-Patterns in Database Applications

III：小：数据库应用程序中反模式的自动检测和解决

基本信息

批准号：
1908984
负责人：
Joy Arulraj
金额：
$ 50万
依托单位：
Georgia Tech Research Corporation
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1908984&HistoricalAwards=false
关键词：
III Small Automatic Detection Resolution

项目摘要

Designing and deploying data-intensive applications is easier now than it ever has been due to the proliferation of data science and database-as-a-service platforms. Data scientists can create applications in a short amount of time that have the potential to reach millions of users and that need to efficiently operate on large amounts of data. Designing database applications is, however, non-trivial since scientists can unknowingly fall into the trap of using an intuitive solution to a problem that is ineffective and often counterproductive, a so called "anti-pattern," thus violating fundamental design principles. The goal of this project is to improve the quality of database applications through a new holistic approach to automatically finding, ranking, and fixing anti-patterns. Studying and developing these techniques is essential in order to support future data science applications that need to efficiently process large amounts of data. The proposed research will make it easier for data scientists to develop applications that: (1) support much larger data sets and more complex workloads; (2) can evolve with less maintenance effort; and (3) are more accurate and secure than what is possible today. As a result, this will accelerate data science and reduce the labor cost of designing and maintaining database applications.The technical aims of the project are divided into three interacting research thrusts: (1) finding anti-patterns in database applications with high precision and recall; (2) ranking the impact of different anti-patterns on performance, maintainability, accuracy, and security; and (3) fixing anti-patterns by altering the logical and physical design of the database and rewriting queries. This research will develop new mechanisms for automated detection and resolution of anti-patterns that go beyond what is achievable in existing systems. The proposed techniques will enable data scientists to develop more performant, maintainable, accurate, and secure applications and will be implemented in a new open-source toolchain. The techniques and methods developed from this research will offer benefits outside of the context of database management systems because it will remove a significant impediment in deriving the full benefits of data-driven decision-making applications. This research effort will advance the understanding of applying program analysis and refactoring techniques to improve database applications.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.

由于数据科学和数据库即服务平台的激增，现在设计和部署数据密集型应用程序比以往任何时候都要容易。数据科学家可以在很短的时间内创建应用程序，这些应用程序有可能覆盖数百万用户，并且需要对大量数据进行有效操作。然而，设计数据库应用程序并非易事，因为科学家可能在不知不觉中陷入使用直观的解决方案来解决无效且通常适得其反的问题的陷阱，即所谓的“反模式”，从而违反了基本的设计原则。这个项目的目标是通过一种新的整体方法来自动查找、排序和修复反模式，从而提高数据库应用程序的质量。为了支持未来需要有效处理大量数据的数据科学应用，研究和开发这些技术是必不可少的。拟议的研究将使数据科学家更容易开发应用程序：(1)支持更大的数据集和更复杂的工作负载；(2)可以以较少的维护工作量进行进化；(3)比目前的技术更准确、更安全。因此，这将加速数据科学，并减少设计和维护数据库应用程序的人工成本。该项目的技术目标分为三个相互作用的研究重点：(1)在数据库应用程序中寻找高精度和高召回率的反模式；(2)对不同反模式对性能、可维护性、准确性和安全性的影响进行排序；(3)通过改变数据库的逻辑和物理设计以及重写查询来修复反模式。这项研究将开发新的机制，用于自动检测和解决反模式，这在现有系统中是无法实现的。所提出的技术将使数据科学家能够开发更高性能、可维护、准确和安全的应用程序，并将在一个新的开源工具链中实现。从这项研究中开发的技术和方法将提供数据库管理系统上下文之外的好处，因为它将消除在获得数据驱动的决策应用程序的全部好处方面的一个重大障碍。这项研究工作将促进应用程序分析和重构技术来改进数据库应用程序的理解。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。