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）是一个普遍应用的框架，用于分析非常大的数据集。在统计ML中，目标是学习一个统计模型，该模型可用于了解数据，查找模式或做出预测。 因此，许多新的软件系统旨在支持在大型数据集上轻松实现和快速执行并行/分布式ML计算机代码。 几乎所有这些系统都是“非关系”的，因为它们利用与当今关系数据库管理系统大不相同的数据和编程模型。 尽管如此，关系或以数据库为导向的数据处理的吸引力仍然存在。例如，在数据库之上运行的代码是声明性的，因此程序员只需要关注他或她想要的东西，而不是如何获得它。这使得编写代码并让它们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，硬件和索引甚至在数据库架构之间进行了强大的分离。 此外，世界上许多结构化数据都位于关系数据库中，除了大型数据集的小型子样本外，要提取任何内容通常是非启动器。能够使用数据库引擎在数据库中执行ML推理代码，将大大提高统计ML的适用性。 该项目将进行必要的基础研究，以使数据库成为成熟的技术。项目开发的所有想法将在SIMSQL的背景下进行原型，评估和分发，这是一个平行的，关系数据库系统，具有执行“随机分析”的能力增强。这意味着SIMSQL具有特殊的设施，可以允许用户定义具有模拟数据的特殊数据库表 - 这些数据实际上并未存储在数据库中，但通过调用统计分布来生成。 由于SIMSQL中的模拟数据表可以具有此类递归依赖性，因此可以轻松地使用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计算提供的优化机会来提高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（Markov Chain Monte Carlo）和Monte Carlo EM（期望最大化）。此外，该项目将自动研究将R和类似错误的ML算法规范编译到SIMSQL SQL中。该项目开发的所有软件将根据Apache许可证提供开源。 该代码可以从（可以在）http://cmj4.web.rice.edu/simsql/simsql.html下载（可以找到更多信息）