BIGDATA: F: DKM: DKA: Big Data Modeling and Analysis with Depth and Scale
BIGDATA:F:DKM:DKA:深度和规模的大数据建模和分析
基本信息
- 批准号:1447549
- 负责人:
- 金额:$ 150万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2014
- 资助国家:美国
- 起止时间:2014-08-01 至 2020-07-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
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.
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但抽象的表示。为了理解一种现象,数据分析师需要能够提出一个模型,评估所提出的模型如何解释数据,并在新数据可用时改进模型。统计模型指定随机变量之间的关系,传统上被用来理解大量有噪声的数据。逻辑模型已广泛应用于数据库和知识库中,用于组织和推理大型和复杂的数据集。该项目旨在开发一种编程语言和系统,用于创建、评估和改进统计和逻辑模型,以明确理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。本项目开发的工具将有助于培训能够将数据转化为知识以供跨学科使用的新一代科学家。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,对精选的本科生进行大数据建模和分析培训;研究生通过课程修改,包括数据科学专业。该项目将开发Px,一种具有良好定义的声明性语义的语言,以支持高级模型构建和分析。Px将能够表达生成和判别概率和关系模型,并且Px系统将支持对这些模型的复杂查询。该项目将包括三个重要且互补的研究方向,旨在发展:(1)语义基础,包括对具有丰富逻辑和统计结构的复杂模型进行简洁规范所需的语言结构;(2)结合精确和近似方法的可扩展推理技术,以及基于逻辑/统计组合模型的查询优化;(3)编程扩展以及静态和动态分析,以支持复杂模型的创建和细化。Px语言和系统将通过两个重要而多样的应用问题进行评估:(1)分析和验证无限状态概率系统,包括参数化系统;(2)从现象数据构建系统发育树,用于生命之树项目,用于绘制生物体的进化史。该项目预计将为创建一个结合概率推理、逻辑推理和约束处理的统一框架做出重大贡献,并强调语义清晰度、效率和可扩展性。该项目还将通过从大数据中开发复杂模型来展示所提出的集成框架的实际效用,这些模型从根本上利用了该技术。
项目成果
期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Founded semantics and constraint semantics of logic rules
- DOI:10.1093/logcom/exaa056
- 发表时间:2020-10
- 期刊:
- 影响因子:0
- 作者:Yanhong A. Liu;S. Stoller
- 通讯作者:Yanhong A. Liu;S. Stoller
Knowledge of Uncertain Worlds: Programming with Logical Constraints: An Overview.
不确定世界的知识:具有逻辑约束的编程:概述。
- DOI:10.1007/978-3-030-36755-8_8
- 发表时间:2020
- 期刊:
- 影响因子:0
- 作者:Liu, Yanhong A.;Stoller, Scott D.
- 通讯作者:Stoller, Scott D.
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Coimbatore Ramakrishnan其他文献
Coimbatore Ramakrishnan的其他文献
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{{ truncateString('Coimbatore Ramakrishnan', 18)}}的其他基金
CT-ISG: Deductive Spreadsheets for Security Policy Specification and Analysis
CT-ISG:用于安全策略规范和分析的演绎电子表格
- 批准号:
0627447 - 财政年份:2006
- 资助金额:
$ 150万 - 项目类别:
Continuing Grant
ITR: Model Checking for Detecting Computer System Vulnerabilities
ITR:用于检测计算机系统漏洞的模型检查
- 批准号:
0205376 - 财政年份:2002
- 资助金额:
$ 150万 - 项目类别:
Continuing Grant
CAREER: Tabled Logic Programming for Verification and Program Analysis
职业:用于验证和程序分析的表格逻辑编程
- 批准号:
9876242 - 财政年份:1999
- 资助金额:
$ 150万 - 项目类别:
Continuing Grant
CISE Postdoctoral Research Associates in Experimental Computer Science: Demand Propagation in Labeled Logic Programming Systems
CISE 实验计算机科学博士后研究员:标记逻辑编程系统中的需求传播
- 批准号:
9901602 - 财政年份:1999
- 资助金额:
$ 150万 - 项目类别:
Standard Grant
CISE PostDoc: Beyond Finite State Model Checking in LMC
CISE 博士后:LMC 中超越有限状态模型检查
- 批准号:
9805735 - 财政年份:1998
- 资助金额:
$ 150万 - 项目类别:
Standard Grant
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