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

统计机器学习(ML)是分析超大数据集的常用框架。在统计ML中，目标是学习一种可用于理解数据、发现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成支持并行/分布式ML计算机代码在大数据集上的容易实现和快速执行。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型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依然存在。例如，在数据库顶部运行的代码是声明性的，因此程序员只需要关心他或她想要什么，而不是如何获得它。这使得编写代码并使其在分布式环境中运行变得更容易，从而实现了代码与数据库数据处理算法、存储、硬件和索引之间的强分离，甚至与数据库架构之间的分离。此外，世界上的大部分结构化数据都位于关系数据库中，从大型数据集中提取任何超过一个小的子样本以供外部使用通常都是不可能的。使用数据库引擎，能够在数据库内执行ML推理代码，将极大地提高统计ML的适用性。该项目将进行必要的基础研究，使数据库中的ML成为一项成熟的技术。该项目开发的所有想法都将在SimSQL的环境中进行原型、评估和分发，SimSQL是一个并行的关系型数据库系统，具有执行“随机分析”的能力。这意味着SimSQL具有特殊的工具，允许用户定义具有模拟数据的特殊数据库表-这些数据并不实际存储在数据库中，而是通过调用统计分布产生的。由于SimSQL中的模拟数据表可以具有这样的递归依赖关系，因此可以很容易地使用SimSQL来运行随机ML推理算法(如MCMC)在“大数据”上。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很容易地用SimSQL的SQL方言指定的ML推理算法的类型，使SimSQL适用于各种随机推理算法，如MCMC(马尔可夫链蒙特卡罗)和蒙特卡罗EM(期望最大化)。此外，该项目将研究将R和类似错误的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阿帕奇许可下开放源码。代码可以从http://cmj4.web.rice.edu/SimSQL/SimSQL.html下载(更多信息请访问)

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