III: Medium: Linear Algebra Operators in Databases to Support Analytic and Machine-Learning Workloads

III：中：数据库中的线性代数运算符支持分析和机器学习工作负载

• 批准号: 2312991
• 负责人: Kenneth Ross
• 金额: $ 101.63万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312991&HistoricalAwards=false
• 关键词: III; Medium; Linear; Algebra; Operators

Machine-learning tools have become ubiquitous in modern information systems. The data inputs used by these tools often originate from relational databases. The data outputs generated by those tools are often stored in databases, where they can be used for subsequent data analysis. Typically, however, the learning process itself is performed outside the database system. This project investigates the opportunity for performing more of the machine learning work within the database itself, avoiding expensive (and often redundant) data export and import. In partnership with researchers from Relational-AI and Microsoft, the Columbia University team will design and build two interacting open-source systems named MARQUE and ZORK. These systems will make data analysis more efficient and effective for database-resident information. Improved efficiency will lead to faster, more cost-effective machine learning, and executing ML within the DBMS will simplify operational complexity and benefit from DBMS features such as scalability, access control, and data management. Ultimately, this work will broaden the adoption of machine learning technologies in a wide range of data-intensive disciplines.MARQUE will be a database management system that supports machine learning primitives such as linear algebra operations within the context of a query processing engine. The system will efficiently compile SQL queries using embedded machine learning models within the database, combining state-of-the-art query processing techniques with highly engineered linear algebra algorithms. MARQUE will allow components of the machine-learning pipeline itself to be formulated as in-database operations, avoiding unnecessary data copying. Conventional SQL analytic queries that can be reformulated using extensions of operators like matrix multiplication can be optimized to use efficient execution plans involving specialized algorithms for such operators. To further support in-database machine learning, the project investigators will build ZORK, a system to support machine learning at scale that will make use of the infrastructure provided by MARQUE. ZORK will scale to very large datasets by processing factorized representations of the data rather than explicitly materializing large joins. This project will develop new and innovative query processing techniques for queries involving both conventional relational operators and generalized linear algebra operators. Tight integration will facilitate query optimization within and between operators. Using this system, a range of machine learning techniques will be developed that operate entirely within the database management system, avoiding data export and simplifying concerns such as data privacy administration.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.

机器学习工具在现代信息系统中已变得无处不在。这些工具使用的数据输入通常来自关系数据库。这些工具生成的数据输出通常存储在数据库中，可以将它们用于后续数据分析。但是，通常，学习过程本身是在数据库系统之外进行的。该项目调查了在数据库本身中执行更多机器学习工作的机会，避免了昂贵（并且通常是冗余）数据导出和导入的机会。 哥伦比亚大学团队将与关系-AI和Microsoft的研究人员合作，设计并建立两个互动的开源系统，名为Marque and Zork。这些系统将使数据分析对数据库居民信息更有效。提高的效率将导致更快，更具成本效益的机器学习，并且在DBMS中执行ML将简化运行复杂性并受益于DBMS功能，例如可扩展性，访问控制和数据管理。最终，这项工作将扩大在广泛的数据密集度学科中的机器学习技术的采用。Marque将是一个数据库管理系统，该系统支持机器学习原始素，例如在查询处理引擎的背景下进行线性代数操作。该系统将使用数据库中的嵌入式机器学习模型有效地编译SQL查询，将最新的查询处理技术与高度工程的线性代数算法相结合。 Marque将允许机器学习管道本身的组件作为数据库操作配制，以避免不必要的数据复制。可以优化使用矩阵乘法（例如矩阵乘法）的扩展来重新重新重新重新重新重新重新重新调整的SQL分析查询，以使用涉及该操作员的专业算法的有效执行计划。为了进一步支持数据库内的机器学习，项目调查人员将建立Zork，这是一个规模支持机器学习的系统，该系统将利用Marque提供的基础设施。 Zork将通过处理数据的分解表示，而不是明确实现大型连接来扩展到非常大的数据集。该项目将开发新的创新查询处理技术，以涉及传统关系运营商和广义线性代数运营商的查询。紧密的集成将有助于操作员之间和之间的查询优化。使用该系统，将开发一系列的机器学习技术，这些技术完全在数据库管理系统中运行，避免数据导出并简化了诸如数据隐私管理等问题。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来支持的。