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，需要对不同背景下各种实体之间的复杂关系和相互作用有更深入的了解。在复杂系统中，(a)发现一个对象如何在特定环境中影响其他对象是至关重要的，而不是寻求影响的总体衡量标准；(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情境敏感影响发现（CSID）奠定了基础，填补了大数据驱动决策在许多关键应用领域的重要空白，包括流行病防范、生物途径分析、气候和弹性水/能源基础设施。因此，它使具有重大经济和健康影响的应用和服务成为可能。该项目的教育影响包括对研究生和本科生的指导，以及通过将研究挑战和成果纳入现有课程，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，以捕获复杂系统中上下文敏感的影响，通常建模为图形，并设计了有效的学习算法，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。上下文敏感影响的建模考虑了相关上下文和不同应用程序的动态特性。这需要解决几个主要挑战，包括潜在的影响背景、实体的异构网络、不同背景下影响的动态性，以及上下文敏感的影响发现的高计算和I/O成本。因此，该项目设计了新颖的可扩展概率和基于张量的算法来捕获和表示上下文敏感的影响。这些算法及其部署的新型数据平台在离线和在线运行时间以及空间要求方面具有高效和可扩展性。为了实现必要的可扩展性，开发的平台采用了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了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.