III: Medium: SimSQL: A Database System Supporting Implementation and Execution of Distributed Machine Learning Codes

III：媒介：SimSQL：支持分布式机器学习代码实现和执行的数据库系统

• 批准号: 1409543
• 负责人: Christopher Jermaine
• 金额: $ 120万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2020-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1409543&HistoricalAwards=false
• 关键词: III; Medium; SimSQL; Database; System

Statistical machine learning (ML) is a commonly-applied framework for analyzing very large data sets. In statistical ML, the goal is to learn a statistical model that can be used to understand the data, find patterns, or make predictions. Thus, many new software systems have been designed to support easy implementation and fast execution of parallel/distributed ML computer codes over large data sets. Almost all of those systems are "non-relational" in the sense that they utilize data and programming models that are very different from today's relational database management systems. Still, the attractiveness of the relational or database-oriented approach to data processing persists. For example, codes running on top of a database are declarative, so a programmer need only be concerned with what he or she wants, and not how to obtain it. This makes it easier to write codes and get them to run in a distributed environment, enabling a strong separation between the code and the database data processing algorithms, storage, hardware, and indexing, and even from the database schema. Further, much of the world's structured data sits in relational databases, and extracting anything more than a small subsample of a large data set for external use is typically a non-starter. Being able to execute a ML inference code within the database, using the database engine, would greatly increase applicability of statistical ML. This project will perform the fundamental research necessary to make ML-in-the-database a mature technology. All of the ideas developed by the project will be prototyped, evaluated, and distributed within the context of SimSQL, which is a parallel, relational database system, augmented with the ability to perform "stochastic analytics". This means that SimSQL has special facilities that allow a user to define special database tables that have simulated data---these are data that are not actually stored in the database, but are produced by calls to statistical distributions. Since tables of simulated data in SimSQL can have such recursive dependencies, it is easy to use SimSQL to run stochastic ML inference algorithms (such as MCMC) over "Big Data". Research tasks include increasing the level of performance of SimSQL by exploiting the optimization opportunities presented by large-scale, iterative, ML computations. They also include expanding the types of ML inference algorithms that can easily be specified in SimSQL's SQL dialect, making SimSQL applicable for various stochastic inference algorithms such as MCMC (Markov Chain Monte Carlo) and Monte Carlo EM (Expectation Maximization). Further, the project will investigate automatically compiling R and BUGS-like ML algorithm specifications into SimSQL SQL. All of the software developed by the project will be available open source under the Apache license. The code can be downloaded from (and more information can be found at) http://cmj4.web.rice.edu/SimSQL/SimSQL.html

统计机器学习 (ML) 是一种用于分析超大型数据集的常用框架。在统计机器学习中，目标是学习可用于理解数据、查找模式或进行预测的统计模型。 因此，许多新的软件系统被设计为支持在大数据集上轻松实现和快速执行并行/分布式机器学习计算机代码。 几乎所有这些系统都是“非关系型”的，因为它们使用的数据和编程模型与当今的关系数据库管理系统非常不同。 尽管如此，关系或面向数据库的数据处理方法的吸引力仍然存在。例如，在数据库之上运行的代码是声明性的，因此程序员只需关心他或她想要什么，而不需要关心如何获取它。这使得编写代码并使其在分布式环境中运行变得更加容易，从而使代码与数据库数据处理算法、存储、硬件和索引甚至与数据库模式之间实现了强有力的分离。 此外，世界上大部分结构化数据都位于关系数据库中，提取大数据集中的一小部分样本以供外部使用通常是不可能的。能够使用数据库引擎在数据库内执行机器学习推理代码，将大大提高统计机器学习的适用性。 该项目将进行必要的基础研究，使数据库中的机器学习成为一项成熟的技术。该项目开发的所有想法都将在 SimSQL 环境中进行原型设计、评估和分发，SimSQL 是一个并行的关系数据库系统，增强了执行“随机分析”的能力。这意味着 SimSQL 具有特殊的功能，允许用户定义具有模拟数据的特殊数据库表——这些数据实际上并不存储在数据库中，而是通过调用统计分布生成的。 由于 SimSQL 中的模拟数据表可以具有此类递归依赖性，因此可以轻松使用 SimSQL 在“大数据”上运行随机 ML 推理算法（例如 MCMC）。研究任务包括通过利用大规模迭代 ML 计算带来的优化机会来提高 SimSQL 的性能水平。它们还包括扩展可以在 SimSQL 的 SQL 方言中轻松指定的 ML 推理算法的类型，使 SimSQL 适用于各种随机推理算法，例如 MCMC（马尔可夫链蒙特卡罗）和蒙特卡罗 EM（期望最大化）。此外，该项目还将研究将 R 和类似 BUGS 的 ML 算法规范自动编译到 SimSQL SQL 中。该项目开发的所有软件都将在 Apache 许可证下开源。 代码可以从 http://cmj4.web.rice.edu/SimSQL/SimSQL.html 下载（更多信息请参见）