III: Small: Simultaneous Decomposition and Predictive Modeling on Large Multi-Modal Data

III：小型：大型多模态数据的同时分解和预测建模

• 批准号: 1017614
• 负责人: Joydeep Ghosh
• 金额: $ 48.93万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1017614&HistoricalAwards=false
• 关键词: III; Small; Simultaneous; Decomposition; Predictive

Several modern data mining applications involve predictive modeling on large amounts of multi-relational data with added structures such as product hierarchies or social networks among customers. The broad goal of this proposal is to develop a comprehensive framework for predictive modeling on large, heterogeneous, multi-relational data based on "Simultaneous Decomposition and Prediction" (SDaP) approaches that iteratively partition the problem into more homogeneous and manageable pieces while concurrently building multiple predictive models, one for each piece. Such approaches lead to simpler and more accurate solutions. The proposed algorithmic strategies that determine how many models to learn and where they should apply, which data to discard and which to keep, how to learn multiple related tasks defined on multi-modal data, and how to scalably implement the solutions on distributed computers, provide practical solutions to certain real-world problems for which current learning and data mining techniques are severely lacking. Application domains of ecology, bio-informatics, market research and web mining are specifically identified and targeted. There are two broad research impacts of the proposed project: (a) it further vitalizes the research in data mining towards better algorithms for predictive modeling on rich and heterogeneous multi-modal data, and (b) provides and promotes the SDaP approach as a fundamental data analysis tool across multiple disciplines. The PI will organize a workshop and offer a tutorial at major data mining conferences to foster and promote research on various aspects of SDaP analysis. Moreover, the curated complex datasets and software developed under this project will be shared with the scientific community via a public web site as part of the proposed one-of-a-kind multi-relational data benchmarking facility. The PI will further develop a novel graduate course on Modeling and Analysis of Complex Data. Outreach modules that illustrate data analysis concepts and capabilities at levels appropriate for pre-college students will also be developed. For further information see the project web site at the URL:http://www.ideal.ece.utexas.edu/projects/sdap/

一些现代数据挖掘应用程序涉及对大量多关系数据进行预测建模，这些数据具有附加结构，如产品层次结构或客户之间的社交网络。该建议的主要目标是基于“同时分解和预测”（SDaP）方法开发一个全面的框架，用于对大型、异构、多关系数据进行预测建模，该方法迭代地将问题划分为更同质和可管理的部分，同时同时构建多个预测模型，每个部分一个。这样的方法导致更简单和更准确的解决方案。所提出的算法策略决定了学习多少模型以及应该应用在哪里，哪些数据应该丢弃，哪些数据应该保留，如何学习多模态数据上定义的多个相关任务，以及如何在分布式计算机上可扩展地实现解决方案，为当前学习和数据挖掘技术严重缺乏的某些现实问题提供了实用的解决方案。生态学、生物信息学、市场研究和网络挖掘的应用领域被明确地确定和定位。提议的项目有两个广泛的研究影响：(a)它进一步激活了数据挖掘的研究，以更好地对丰富和异构多模态数据进行预测建模；(b)提供并促进了SDaP方法作为跨多学科的基本数据分析工具。PI将在主要的数据挖掘会议上组织研讨会并提供教程，以促进和促进对SDaP分析的各个方面的研究。此外，在这个项目下开发的精心策划的复杂数据集和软件将通过一个公共网站与科学界共享，作为拟议的独一无二的多关系数据基准设施的一部分。PI将进一步开发一门关于复杂数据建模和分析的新研究生课程。还将开发适合大学预科学生的外展模块，说明数据分析概念和能力。欲了解更多信息，请参阅项目网站的URL:http://www.ideal.ece.utexas.edu/projects/sdap/