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

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在特定环境中影响其他对象，而不是寻求对影响的总体衡量，（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决策的重要空白，包括流行病准备，生物途径分析，气候和弹性水/能源基础设施。因此，它使应用程序和服务具有重大的经济和健康影响。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课程，增强亚利桑那州立大学（ASU）和新墨西哥州州立大学（NMSU）的研究生和本科计算机科学课程。该项目的技术目标是建立复杂系统中大数据驱动的上下文敏感影响发现的理论，算法和计算基础。该项目开发了基于概率和张量的模型，以捕获复杂系统的上下文敏感影响，通常建模为图形，并设计了有效的学习算法，可以捕获这些不同上下文中实体之间的上下文和影响分数。上下文敏感影响的建模考虑了相关上下文的动态性质和不同的应用。这需要解决几个主要的挑战，包括潜在的影响，异构网络的实体，在不同的环境中的影响的动态性，以及上下文敏感的影响发现的高计算和I/O成本。因此，本项目设计了新颖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。这些算法和新的数据平台，他们部署在离线和在线运行时间和空间要求方面是有效的和可扩展的。为了实现必要的可扩展性，开发的平台采用新的多分辨率数据分区和资源分配策略，研究通过新的基于非易失性存储器的数据管理技术实现大规模并行和高效的数据访问。

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.