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: 1633271
• 负责人: Tingjian Ge
• 金额: $ 25.74万
• 依托单位: University of Massachusetts Lowell
• 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 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1633271&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。新兴应用程序需要计算高效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以有效支持下一代图数据分析，尤其是在计算资源有限的情况下。 在该项目中，PI 开发模型、算法和工具来支持资源限制下大规模图数据的关联分析。 该项目制定了传统关联模型的新变体，并通过图形查询的高级功能得到增强。探索精确和近似查询和挖掘范式，以支持对多源、大规模和快速变化的图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) 多图关联分析和 b) 图流上的关联检测。该项目开发了一套由所提出的通用模型和算法支持的分布式和流关联挖掘技术。增强的模型和算法使得能够在广泛的海量数据应用中进行可扩展的关联分析。从该项目中学到的原理通常可以应用于大数据分析和系统设计。新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具有直接应用。该项目的应用合作者包括太平洋西北国家实验室、LogicMonitor 和 Facebook。该项目更广泛的影响还包括对包括女性和少数民族在内的学生的研究培训和教育，以及针对计算机科学和非计算机科学学生的新课程和教育工具的设计。

项目成果

Finding Densest Lasting Subgraphs in Dynamic Graphs: A Stochastic Approach

在动态图中查找最密集的持久子图：随机方法

• DOI: 10.1109/icde.2019.00075
• 发表时间: 2019-04-01
• 期刊: 2019 IEEE 35th International Conference on Data Engineering (ICDE)
• 作者: Xuanming Liu;Tingjian Ge;Yinghui Wu
• 通讯作者: Yinghui Wu

A Stochastic Approach to Finding Densest Temporal Subgraphs in Dynamic Graphs

寻找动态图中最密集时间子图的随机方法

• DOI: 10.1109/tkde.2020.3025463
• 发表时间: 2020-01
• 期刊: IEEE Transactions on Knowledge and Data Engineering
• 影响因子: 8.9
• 作者: Liu, Xuanming;Ge, Tingjian;Wu, Yinghui
• 通讯作者: Wu, Yinghui

Labeled Graph Sketches

标记图草图

• DOI: 10.1109/icde.2018.00138
• 发表时间: 2018-04-01
• 期刊: 2018 IEEE 34th International Conference on Data Engineering (ICDE)
• 作者: Chunyao Song;Tingjian Ge
• 通讯作者: Tingjian Ge

Selective Edge Shedding in Large Graphs Under Resource Constraints

资源约束下大图的选择性边缘脱落

• DOI: 10.1109/icde51399.2021.00200
• 发表时间: 2021-04-01
• 期刊: 2021 IEEE 37th International Conference on Data Engineering (ICDE)
• 作者: Yiling Zeng;Chunyao Song;Tingjian Ge
• 通讯作者: Tingjian Ge

Online Indices for Predictive Top-k Entity and Aggregate Queries on Knowledge Graphs

用于预测 Top-k 实体的在线索引和知识图上的聚合查询

• DOI: 10.1109/icde48307.2020.00096
• 发表时间: 2020-04-01
• 期刊: 2020 IEEE 36th International Conference on Data Engineering (ICDE)
• 作者: Yan Li;Tingjian Ge;Cindy X. Chen
• 通讯作者: Cindy X. Chen