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

几个现代数据挖掘应用程序涉及对大量多关系数据的预测建模，并增加了客户之间的产品层次结构或社交网络等结构。这项提议的主要目标是开发一个全面的框架，用于基于“同时分解和预测”(SDaP)方法的大型、异质、多关系数据的预测建模，该方法迭代地将问题划分为更同质和更易于管理的部分，同时同时建立多个预测模型，每个部分一个。这些方法导致了更简单和更准确的解决方案。提出的算法策略决定了要学习多少个模型和它们应该应用到哪里，要丢弃哪些数据和保留哪些数据，如何学习定义在多模式数据上的多个相关任务，以及如何在分布式计算机上可扩展地实施这些解决方案，为当前学习和数据挖掘技术严重缺乏的某些现实世界问题提供了实用的解决方案。明确了生态学、生物信息学、市场研究和网络挖掘的应用领域。拟议项目有两个广泛的研究影响：(A)它进一步促进了数据挖掘方面的研究，以便更好地对丰富和不同种类的多模式数据进行预测建模；(B)提供并促进SDaP方法，将其作为跨多个学科的基本数据分析工具。国际和平研究所将组织一次讲习班，并在主要的数据挖掘会议上提供指导，以促进和促进对SDAP分析各个方面的研究。此外，在该项目下开发的经过管理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。还将开发推广模块，在适合大学预科学生的水平上说明数据分析的概念和能力。欲了解更多信息，请访问项目网站URL:http://www.ideal.ece.utexas.edu/projects/sdap/

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/