BIGDATA: Collaborative Research: F: Association Analysis of Big Graphs: Models, Algorithms and Applications

BIGDATA：协作研究：F：大图关联分析：模型、算法和应用

• 批准号: 1633629
• 负责人: Yinghui Wu
• 金额: $ 32.17万
• 依托单位: Washington State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1633629&HistoricalAwards=false
• 关键词: BIGDATA; Collaborative; Research; Association; Analysis

Association analysis is a fundamental problem in Big Data analytics. Emerging applications require computationally efficient association models and scalable association mining techniques to find regularities of graph data. Conventional association analysis for transactional data is hard or infeasible to be adapted to effectively support the next generation of graph data analytics, especially under limited computing resources. In this project, the PIs develop models, algorithms and tools to support association analysis over large-scale graph data under resource constraints. The project formulates new variants of the conventional association model that are enhanced by advanced capability of graph queries. Both exact and approximate querying and mining paradigms are explored to support effective association analysis over multi-source, large-scale, and fast-changing graph data. The PIs instantiate the generic framework to two practical association analysis scenarios, notably, a) multi-graph association analysis, and b) association detection over graph streams. The project develops a package of distributed and stream association mining techniques supported by the proposed generic model and algorithms.The enhanced model and algorithms enable scalable association analysis in a wide range of massive data applications. The principles learned from this project can be applied to big data analytics and system design in general. The study of new association analysis framework has immediate applications in emerging areas, including data quality, affinity marketing, and network security. Application collaborators of the project include Pacific Northwest National Laboratory, LogicMonitor, and Facebook. Broader impacts of the project also include research training and education of students including women and minorities, and design of new curricula and education tools that target both CS and non-CS students.

关联分析是大数据分析中的一个基本问题。新兴的应用需要计算效率高的关联模型和可扩展的关联挖掘技术来发现图数据的规律性。传统的事务数据关联分析很难或不可行地适用于有效地支持下一代图数据分析，特别是在计算资源有限的情况下。在这个项目中，PI开发了模型、算法和工具，以支持在资源受限的情况下对大规模图形数据进行关联分析。该项目制定了传统关联模型的新变体，并通过高级图形查询功能进行了增强。探索了精确和近似的查询和挖掘范例，以支持对多源、大规模和快速变化的图形数据进行有效的关联分析。PI将通用框架实例化为两个实际的关联分析场景，特别是a)多图关联分析，以及b)图流上的关联检测。该项目开发了一套分布式和流关联挖掘技术，在所提出的通用模型和算法的支持下，增强的模型和算法能够在广泛的海量数据应用中实现可伸缩的关联分析。从这个项目中学到的原则一般可以应用于大数据分析和系统设计。新的关联分析框架的研究在数据质量、亲和力营销和网络安全等新兴领域都有直接的应用。该项目的应用程序协作者包括太平洋西北国家实验室、LogicMonitor和Facebook。该项目的更广泛影响还包括对包括妇女和少数群体在内的学生的研究、培训和教育，以及设计新的课程和教育工具，既针对CS学生，也针对非CS学生。