BIGDATA: F: DKM: DKA: Big Data Modeling and Analysis with Depth and Scale

理解大量数据的一个重要步骤是构建模型：对产生数据的现象进行简洁而抽象的表示。为了理解现象，数据分析师需要能够提出模型，评估建议的模型如何解释数据，并在新数据可用时改进模型。统计模型规定了随机变量之间的关系，传统上被用来理解大量的噪声数据。逻辑模型已被广泛用于数据库和知识库中，用于组织和推理大型且复杂的数据集。该项目旨在开发一种程序设计语言和系统，用于创建、评价和改进综合统计和逻辑模型，以明确了解非常庞大和复杂的数据集。除了对大数据问题的模型开发产生直接影响外，该项目产生的语义基础和可扩展计算基础设施预计将直接影响系统开发和验证、规划和优化领域，在科学和工程领域具有广泛的应用。该项目开发的工具将有助于培训能够将数据转化为跨学科使用的知识的新一代科学家。该项目的教育和推广部分旨在通过年度研讨会和研究指导，培训精选的本科生大数据建模和分析；以及通过修改课程，包括数据科学专业，培训研究生。该项目将开发PX，一种具有良好定义的说明性语义的语言，以支持高级模型构建和分析。PX将能够表示生成性和区别性概率模型和关系模型，并且PX系统将支持对此类模型的复杂查询。该项目将包括三个重要和相辅相成的研究方向，旨在发展：(1)语义基础，包括简洁说明具有丰富逻辑和统计结构的复杂模型所需的语言结构；(2)结合精确和近似方法的可扩展推理技术，以及对组合逻辑/统计模型的查询优化；(3)支持创建和改进复杂模型的编程扩展以及静态和动态分析。PX语言和系统将使用两个重要和不同的应用问题进行评估：(1)无限状态概率系统的分析和验证，包括参数化系统，以及(2)根据物候学数据构建系统发育树，用于生命之树项目，用于绘制生物进化历史图。预计该项目将为创建一个统一的框架做出重大贡献，该框架结合了概率推理、逻辑推理和约束处理，强调语义清晰、效率和可伸缩性。该项目还将通过从根本上利用这项技术的大数据开发复杂模型，展示拟议的综合框架的实际效用。

An important step in understanding large volumes of data is the construction of a model: a succinct but abstract representation of the phenomenon that produced the data. In order to understand a phenomenon, a data analyst needs to be able to propose a model, evaluate how the proposed model explains the data, and refine the model as new data becomes available. Statistical models, which specify relationships among random variables, have traditionally been used to understand large volumes of noisy data. Logical models have been used widely in databases and knowledge bases for organizing and reasoning with large and complex data sets. This project is aimed at developing a programming language and system for the creation, evaluation and refinement of combined statistical and logical models for the express purpose of understanding very large and complex data sets. Apart from its direct effect on model development for Big Data problems, the semantic foundations and scalable computing infrastructure resulting from this project is expected to directly impact the areas of system development and verification, planning, and optimization, with broad application in Science and Engineering. The tools developed in this project will facilitate the training of a new generation of scientists capable of transforming data into knowledge for use across disciplines. The project's education and outreach component is designed to train select undergraduate students on Big Data modeling and analysis via annual workshops and research mentorship; and graduate students via curriculum modifications including a specialization in Data Science.The project will develop Px, a language with well-defined declarative semantics, to support high-level model construction and analysis. Px will be capable of expressing generative and discriminative probabilistic and relational models, and the Px system will support complex queries over such models. The project will encompass three significant and complementary research directions, aimed at developing: (1) semantic foundations, including language constructs needed for succinct specification of complex models with rich logical and statistical structure; (2) scalable inference techniques combining exact and approximate methods, and query optimizations over combined logic/statistical models; and (3) programming extensions as well as static and dynamic analyses to support the creation and refinement of complex models. The Px language and system will be evaluated using two important and diverse application problems: (1) analysis and verification of infinite-state probabilistic systems, including parameterized systems, and (2) construction of phylogenetic trees from phenomic data, used in the Tree of Life project, for mapping the evolutionary history of organisms. The project is expected to make significant contributions towards creating a unifying framework combining probabilistic inference, logical inference, and constraint processing, with an emphasis on semantic clarity, efficiency, and scalability. The project will also demonstrate the practical utility of the proposed integrated framework by developing complex models from big data that take advantage of this technology in fundamental ways.