Managing Constraints as Data

数据库已经成为数据驱动的数字时代的基石。世界上的事件和观察产生了大量的数据，这些数据被安全地存储在数据库中，并通过数据库查询有效地分析。分析结果进一步支持广泛软件系统的决策制定，例如工资和财务管理、控制系统和运筹学。这些决策必须尊重安全性和正确性约束。约束的例子随处可见：工资截止日期、最大容量、服务器可用性等。虽然数据库技术在数据存储和分析方面表现出色，但目前它无法为约束的管理和验证提供相同水平的有效性。目前，为了填补这一空白，需要程序员手动开发特定于应用程序的程序。但这种努力代价高昂，而且极易出错。由于人为错误和有缺陷的软件而导致的约束违反在行业和政府的不同部门中经常被观察到。这个拟议的研究计划旨在通过扩展数据库的作用来包括约束管理来纠正这种情况。******申请者的目标是扩展数据库的数据模型和查询语言，像数据一样将逻辑约束作为一级公民。这允许定义良好的声明性语义在统一框架中存储和管理约束和数据。该研究计划建立在最先进的人工智能约束满足算法的基础上，旨在增强现有的数据库技术，在存在约束和数据的情况下积极地推理决策，从而减轻对二级安全维护软件的需求。这样的数据库引擎可以验证并主动合成安全、正确和最优的数据驱动决策。******提出的研究方案对未来数据库的理论和系统设计有一定的贡献。对于数据库理论，我们将研究(1)新的约束查询运算符及其属性，(2)管理相互冲突、不完整和时变约束的新算法，以及(3)能够在互联网规模上处理大量约束集合的分布式算法。在约束数据库引擎的系统设计方面，我们将研究(4)约束求解器与数据库引擎的集成，(5)高并发约束求解器的实现，以及(6)交互式和众包约束满足与验证的用户界面设计与实现。***总之，该研究计划的成功将使数据库能够使用声明性查询语言来管理约束。数据库引擎将帮助软件系统做出更安全的决策，这些决策相对于数据库中的约束是可证明正确的。

Database has been a cornerstone in the data-driven digital age. Events and observations in the world generate large volumes of data which are safely stored in databases and efficiently analyzed by database queries. The results of the analysis further support decision making of a wide range of software systems, e.g. payroll and financial management, control systems, and operational research. These decisions must respect safety and correctness constraints. Examples of constraints are everywhere: payroll deadlines, maximal power capacity, server availability etc.. While the database technology excels at data storage and analytics, presently it fails to provide the same level of effectiveness for the management and verification of constraints. Currently, to fill this gap, application specific programs need to be manually developed by programmers. But such efforts are costly and highly error-prone. Constraint violations as the results of human errors and buggy software are frequently observed in different sectors of the industry and governments. This proposed research program is aimed to rectify the situation by extending the role of the database to include the management of constraints.******The applicant's objective is to extend the data model and the query language of databases to include logical constraints as first-class citizens just like data. This allows a well-defined declarative semantics to store and manage constraints and data in a unified framework. Building on top of state-of-art A.I. constraint satisfaction algorithms, the research program is to augment existing database technology to actively reason about decisions in the presence of constraints and data, thus alleviating the need for secondary safety-maintenance software. Such database engines can verify and actively synthesize data-driven decisions that are safe, correct and optimal.******The proposed research program contributes to the theory and system design of future databases. Towards database theory, we will be studying (1) new constraint query operators and their properties, (2) new algorithms to manage mutually conflicting, incomplete and time varying constraints, and (3) distributed algorithms that are capable of processing large collections of constraints at Internet scale. Towards the system design of constraint database engines, we will perform research on (4) integration of constraint solvers with database engines, (5) implementation of highly concurrent constraint solvers, and (6) user interface design and implementation for interactive and crowdsourcing constraint satisfaction and verification.***In summary, the success of the proposed research program will enable databases to manage constraints using a declarative query language. Database engines will help software systems to make safer decisions that are provably correct with respect to the constraints in the database.