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

由于数据科学和数据库即服务平台的激增，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，并且需要有效地处理大量数据。然而，设计数据库应用程序并不简单，因为科学家可能会不知不觉地陷入使用直观解决方案解决问题的陷阱，这种解决方案是无效的，而且往往适得其反，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、排名和修复反模式，从而提高数据库应用程序的质量。研究和开发这些技术对于支持未来需要有效处理大量数据的数据科学应用至关重要。拟议的研究将使数据科学家更容易开发应用程序，这些应用程序：（1）支持更大的数据集和更复杂的工作负载;（2）可以在更少的维护工作量下发展;（3）比今天更准确和安全。该项目的技术目标分为三个相互作用的研究方向：（1）在数据库应用程序中发现具有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）在数据库应用程序中发现具有高精度和高召回率的反模式。以及（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超越现有系统可实现的反模式。所提出的技术将使数据科学家能够开发更高性能、更易维护、更准确和更安全的应用程序，并将在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的背景之外的好处，因为它将消除一个重大障碍，在获得数据驱动的决策应用程序的全部好处。这项研究工作将促进对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NSF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。

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.