DC:Small:Collaborative Research:Data Intensive Computing for General Relational Data Learning

DC:Small:协作研究：用于一般关系数据学习的数据密集型计算

• 批准号: 1018114
• 负责人: Lixin Gao
• 金额: $ 25万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1018114&HistoricalAwards=false
• 关键词: DC; Small; Collaborative; Research; Data

It is well-observed that the whole world is full of data that are highly related and of diverse data object types such as people, organizations, and events. In many applications, it is intended to discover the hidden structures through such relationships involving different types of data objects in the world, in addition to "clusters" of the same type of data objects. On the other hand, relational data learning typically involves a large collection of data objects and thus algorithms for relational data learning are computation-intensive as well as data intensive. This calls for massively parallel solutions in order to make the algorithms scalable to large collections of data. This project addresses a three year integrated research and education program focusing on engaging in-depth research in developing novel parallel frameworks for a wide spectrum of state-of-the-art solutions to a series of fundamental problems in relational data learning. This research promotes the revolutionized understanding of relational data learning in the context of distributed computation environment. The project addresses fundamental problems in the literature of relational data learning as well as the expected breakthrough in the interdisciplinary and multidisciplinary research communities including parallel computation and scheduling, data mining and machine learning, and pattern analysis. The technologies generated from the research can be immediately deployed in important applications such as social network analysis, biological information discovery, financial and economic development analysis and prediction, natural disaster prediction, as well as military intelligence analysis. Project url: http://www.fortune.binghamton.edu/nsf-iis-1017828.htm

众所周知，整个世界充满了高度相关的数据，并且数据对象类型多种多样，例如人员、组织和事件。在许多应用中，除了相同类型的数据对象的“簇”之外，还旨在通过涉及世界上不同类型的数据对象的这种关系来发现隐藏的结构。另一方面，关系数据学习通常涉及大量数据对象的集合，因此关系数据学习的算法是计算密集型和数据密集型的。这需要大规模并行解决方案，以使算法可扩展到大型数据集合。该项目涉及一个为期三年的综合研究和教育计划，重点是深入研究开发新颖的并行框架，为关系数据学习中的一系列基本问题提供最先进的解决方案。这项研究促进了分布式计算环境下关系数据学习的革命性理解。该项目解决了关系数据学习文献中的基本问题，以及跨学科和多学科研究社区的预期突破，包括并行计算和调度、数据挖掘和机器学习以及模式分析。研究产生的技术可以立即部署在社交网络分析、生物信息发现、金融和经济发展分析与预测、自然灾害预测以及军事情报分析等重要应用中。项目网址：http://www.fortune.binghamton.edu/nsf-iis-1017828.htm