Managing Constraints as Data

数据库已经成为数据驱动的数字时代的基石。世界上的事件和观测产生了大量的数据，这些数据被安全地存储在数据库中，并通过数据库查询进行高效的分析。分析结果进一步支持各种软件系统的决策，例如薪金和财务管理、控制系统和业务研究。这些决定必须遵守安全和正确性约束。制约因素的例子比比皆是：工资单截止日期、最大电力容量、服务器可用性等。虽然数据库技术擅长数据存储和分析，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，特定于应用的程序需要由程序员手动开发。但这样的努力代价高昂，而且极易出错。作为人为错误和软件缺陷的结果，违反约束的情况经常出现在行业和政府的不同部门。这项拟议的研究计划旨在通过将数据库的作用扩展到包括约束管理来纠正这种情况。申请人的目标是扩展数据库的数据模型和查询语言，以包括像数据一样的一等公民的逻辑约束。这允许定义良好的声明性语义在统一的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，以便在存在约束和数据的情况下主动推理决策，从而减少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综合安全、正确和最优的数据驱动决策，所提出的研究方案有助于未来数据库的理论和系统设计。在数据库理论方面，我们将研究(1)新的约束查询算子及其性质，(2)管理相互冲突的、不完整的和时变的约束的新算法，以及(3)能够处理因特网规模的大量约束的分布式算法。对于约束数据库引擎的系统设计，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)交互和众包约束满足和验证的用户界面的设计和实现。数据库引擎将帮助软件系统针对数据库中的约束做出可证明是正确的更安全的决策。

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.