BIGDATA: Collaborative Research: F: Discovering Context-Sensitive Impact in Complex Systems

BIGDATA：协作研究：F：发现复杂系统中的上下文敏感影响

• 批准号: 1633330
• 负责人: Huiping Cao
• 金额: $ 36.18万
• 依托单位: New Mexico 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 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1633330&HistoricalAwards=false
• 关键词: BIGDATA; Collaborative; Research; Discovering; Context

Successfully tackling many urgent challenges in socio-economically critical domains (such as sustainability, public health, and biology) requires obtaining a deeper understanding of complex relationships and interactions among a diverse spectrum of entities in different contexts. In complex systems, (a) it is critical to discover how one object influences others within specific contexts, rather than seeking an overall measure of impact, and (b) the context-aware understanding of impact has the potential to transform the way people explore, search, and make decisions in complex systems. This project establishes the foundations of big data driven Context-Sensitive Impact Discovery (CSID) in complex systems and fills an important hole in big data driven decision making in many critical application domains, including epidemic preparedness, biological pathway analysis, climate, and resilient water/energy infrastructures. Thus, it enables applications and services with significant economic and health impact. The educational impacts of this project include the mentoring of graduate and undergraduate students, and the enhancement of graduate and undergraduate Computer Science curricula at both Arizona State University (ASU) and New Mexico State University (NMSU) through the incorporation of research challenges and outcomes into existing classes. The technical goal of this project is to establish the theoretical, algorithmic, and computational foundations of big data driven context-sensitive impact discovery in complex systems. This project develops probabilistic and tensor-based models to capture context-sensitive impact from complex systems, often modeled as graphs, and designs efficient learning algorithms that can capture both the contexts and the impact scores among entities within these different contexts. The modeling of the context sensitive impact considers dynamic nature of relevant contexts and the diverse applications. This requires addressing several major challenges, including latent contexts of impact, heterogeneous networks of entities, dynamicity of impact in varying contexts, and high computational and I/O costs of context-sensitive impact discovery. Therefore, this project designs novel scalable probabilistic and tensor-based algorithms to capture and represent context-sensitive impact. These algorithms and the novel data platforms they are deployed in are efficient and scalable in terms of off-line and on-line running times and their space requirements. To achieve necessary scalabilities, the developed platforms employ novel multi-resolution data partitioning and resource allocation strategies and the research enables massive parallelism and efficient data access through new non-volatile memory based data management techniques.

成功应对社会经济关键领域(如可持续性、公共卫生和生物学)的许多紧迫挑战，需要更深入地了解不同背景下各种实体之间的复杂关系和相互作用。在复杂系统中，(A)关键是要发现一个对象如何影响特定环境中的其他对象，而不是寻求影响的总体衡量标准，以及(B)对影响的上下文感知理解有可能改变人们在复杂系统中探索、搜索和决策的方式。该项目为复杂系统中的大数据驱动的上下文敏感影响发现(CSID)奠定了基础，并填补了许多关键应用领域中大数据驱动的决策的重要漏洞，包括疫情防范、生物路径分析、气候和有弹性的水/能源基础设施。因此，它使具有重大经济和健康影响的应用程序和服务成为可能。该项目的教育影响包括指导研究生和本科生，并通过将研究挑战和成果纳入现有班级，加强亚利桑那州立大学(ASU)和新墨西哥州立大学(NMSU)的研究生和本科生计算机科学课程。该项目的技术目标是为复杂系统中大数据驱动的上下文敏感影响发现奠定理论、算法和计算基础。该项目开发了基于概率和张量的模型来捕获复杂系统的上下文敏感影响，通常建模为图，并设计了高效的学习算法，可以捕获上下文和这些不同上下文中实体之间的影响分数。情境敏感影响的建模考虑了相关情境和不同应用的动态性。这需要解决几个主要挑战，包括潜在的影响背景、不同的实体网络、在不同背景下影响的动态化，以及上下文敏感影响发现的高昂计算和I/O成本。因此，本项目设计了新的可扩展的概率和基于张量的算法来捕获和表示上下文敏感的影响。这些算法和它们所部署的新型数据平台在离线和在线运行时间及其空间要求方面都是高效和可扩展的。为了实现必要的可扩展性，开发的平台采用了新颖的多分辨率数据分区和资源分配策略，研究通过基于非易失性存储器的新数据管理技术实现了大规模并行和高效的数据访问。

项目成果

A New Attention Mechanism to Classify Multivariate Time Series

一种新的注意力机制来对多元时间序列进行分类

• DOI: 10.24963/ijcai.2020/277
• 发表时间: 2020
• 期刊: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
• 作者: Hao, Yifan;Cao, Huiping
• 通讯作者: Cao, Huiping

Sub-Gibbs Sampling: A New Strategy for Inferring LDA

亚吉布斯采样：推断 LDA 的新策略

• DOI: 10.1109/icdm.2017.113
• 发表时间: 2017
• 期刊: Proc. of Intl. Conf. on Data Mining (ICDM
• 作者: Hu, Chuan;Cao, Huiping;Gong, Qixu
• 通讯作者: Gong, Qixu

Multi-criteria and Review-Based Overall Rating Prediction

多标准和基于评论的总体评分预测

• DOI: 10.1007/978-3-030-75765-6_38
• 发表时间: 2021
• 期刊: Pacific-Asia Conference on Knowledge Discovery and Data Mining
• 作者: Ceh-Varela, Edgar;Cao, Huiping;Le, Tuan
• 通讯作者: Le, Tuan

CSQ System: A System to Support Constrained Skyline Queries on Transportation Networks

CSQ 系统：支持交通网络受限天际线查询的系统

• DOI: 10.1109/icde48307.2020.00160
• 发表时间: 2020
• 期刊: Proc. of IEEE Intl. Conf. on Data Engineering (ICDE
• 作者: Gong, Qixu;Liu, Jiefei;Cao, Huiping
• 通讯作者: Cao, Huiping

Identify Significant Phenomenon-Specific Variables for Multivariate Time Series

• DOI: 10.1109/tkde.2019.2934464
• 发表时间: 2019-08
• 期刊: IEEE Transactions on Knowledge and Data Engineering
• 影响因子: 8.9
• 作者: Yifan Hao;H. Cao;A. Mueen;S. Brahma