Frameworks: Collaborative Proposal: Software Infrastructure for Transformative Urban Sustainability Research

框架：合作提案：变革性城市可持续发展研究的软件基础设施

• 批准号: 1931283
• 负责人: Claire Welty
• 金额: $ 41万
• 依托单位: University of Maryland Baltimore County
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931283&HistoricalAwards=false
• 关键词: Frameworks; Collaborative; Proposal; Software; Infrastructure

The United States is highly urbanized with more than 80% of the population residing in cities. Cities draw from and impact natural resources and ecosystems while utilizing vast, expensive infrastructures to meet economic, social, and environmental needs. The National Science Foundation has invested in several strategic research efforts in the area of urban sustainability, all of which generate, collect, and manage large volumes of spatiotemporal data. Voluminous datasets are also made available in domains such as climate, ecology, health, and census. These data can spur exploration of new questions and hypotheses, particularly across traditionally disparate disciplines, and offer unprecedented opportunities for discovery and innovation. However, the data are encoded in diverse formats and managed using a multiplicity of data management frameworks -- all contributing to a break-down of the observational space that inhibits discovery. A scientist must reconcile not only the encoding and storage frameworks, but also negotiate authorizations to access the data. A consequence is that data are locked in institutional silos, each of which represents only a sliver of the observational space. This project, SUSTAIN (Software for Urban Sustainability to Tailor Analyses over Interconnected Networks), facilitates and accelerates discovery by significantly alleviating data-induced inefficiencies. This effort has deep, far-reaching impact. It transforms urban sustainability science by establishing a community of interdisciplinary researchers and catalyzing their collaborative capacity. Hundreds of researchers from over 150 universities are members of our collaborating organizations and will immediately benefit from SUSTAIN. Domains where spatiotemporal phenomena must be analyzed benefit from this innovative research; the partnership with ESRI and Google Earth amplify the impact of SUSTAIN, giving the project a global reach and enabling international collaborative initiatives. The direct engagement with middle school students in computer science and STEM disciplines has well-known benefits and, combined with graduate training, produces a diverse, globally competitive STEM workforce. SUSTAIN targets transformational capabilities for feature space exploration, hypotheses formulation, and model creation and validation over voluminous, high-dimensional spatiotemporal data. These capabilities are deeply aligned with the urban sustainability community's needs, and they address challenges that preclude effective research. SUSTAIN accomplishes these interconnected goals by enabling holistic visibility of the observational space, interactive visualizations of multidimensional information spaces using overlays, fast evaluation of expressive queries tailored to the needs of the discovery process, generation of custom exploratory datasets, and interoperation with diverse analyses software frameworks - all leading to better science. SUSTAIN fosters deep explorations through its transformative visibility of the federated information space. The project reconciles the fragmentation and diversity of siloed data to provide seamless, unprecedented visibility of the information space. A novel aspect of the project's methodology is the innovative use of the Synopsis, a spatiotemporal sketching algorithm that decouples data and information. The methodology extracts and organizes information from the data and uses the information (or sketches of the data) as the basis for explorations. The project also incorporates a novel algorithm for imputations at the sketch level at myriad spatiotemporal scopes. The effort creates a collaborative community of multidisciplinary researchers to build an enduring software infrastructure for urban sustainability.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

美国高度城市化，超过80％的人口居住在城市中。城市利用自然资源和生态系统，同时利用庞大，昂贵的基础设施来满足经济，社会和环境需求。国家科学基金会已投资于城市可持续性领域的几项战略研究工作，所有这些研究都产生，收集和管理大量时空数据。大量数据集也可以在气候，生态，健康和人口普查等领域提供。这些数据可以刺激对新问题和假设的探索，尤其是在传统上不同的学科中，并为发现和创新提供了前所未有的机会。但是，数据以各种格式进行编码，并使用多种数据管理框架进行管理 - 所有这些都导致了抑制发现的观测空间的分解。科学家不仅必须协调编码和存储框架，而且还必须协商授权以访问数据。结果是数据被锁定在机构孤岛中，每个孤岛都仅代表观察空间的一个片段。该项目，维持（城市可持续性的软件，可以根据相互联系的网络量身定制分析），通过大大减轻数据引起的效率低下来促进和加速发现。这项工作具有深远的影响力。它通过建立跨学科研究人员的社区并催化其协作能力来改变城市的可持续发展科学。来自150多名大学的数百名研究人员是我们合作组织的成员，将立即受益于维持。必须从这项创新研究中分析时空现象受益的领域；与ESRI和Google Earth的合作伙伴关系扩大了维持的影响，从而为项目提供了全球影响力并实现了国际协作计划。与中学生在计算机科学和STEM学科中的直接互动具有众所周知的好处，并与研究生培训相结合，产生了多样化的全球竞争性STEM劳动力。维持目标探索特征空间探索，假设公式以及模型创建和验证的目标能力。这些能力与城市可持续发展社区的需求深深地保持一致，它们解决了排除有效研究的挑战。维持通过实现观测空间的整体可见性，使用叠加层的多维信息空间的交互可视化，快速评估根据发现过程的需求量身定制的表达性查询，生成自定义探索性数据集的需求的表达性查询，以及与多样化的软件框架的互操作来实现这些相互联系的目标。通过其联合信息空间的变革性可见性来维持深度探索。该项目调解了孤立数据的分裂和多样性，以提供信息空间的无缝，前所未有的可见性。该项目方法论的一个新颖方面是概要的创新使用，这是一种分解数据和信息的时空草图算法。该方法从数据中提取和组织信息，并使用信息（或数据草图）作为探索的基础。该项目还结合了一种新型算法，用于在众多时空范围的草图级别进行归纳。这项努力创建了一个由多学科研究人员组成的协作社区，以建立一个持久的城市可持续性软件基础设施。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估审查标准来通过评估来获得支持的。

项目成果

A synthetic water distribution network model for urban resilience

城市复原力的综合配水网络模型

• DOI: 10.1080/23789689.2020.1788230
• 发表时间: 2020
• 期刊: Sustainable and Resilient Infrastructure
• 影响因子: 5.9
• 作者: Ahmad, Nasir;Chester, Mikhail;Bondank, Emily;Arabi, Mazdak;Johnson, Nathan;Ruddell, Benjamin L.
• 通讯作者: Ruddell, Benjamin L.

Rapid, Progressive Sub-Graph Explorations for Interactive Visual Analytics over Large-Scale Graph Datasets

• DOI: 10.1145/3365109.3368793
• 发表时间: 2019-12
• 期刊: Proceedings of the 6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies
• 作者: Samuel Armstrong;Kevin Bruhwiler;S. Pallickara

An Interactive Data Quality Test Approach for Constraint Discovery and Fault Detection

• DOI: 10.1109/bigdata47090.2019.9006446
• 发表时间: 2019-12
• 期刊: 2019 IEEE International Conference on Big Data (Big Data)
• 作者: Hajar Homayouni;Sudipto Ghosh;I. Ray;M. Kahn

STASH : Fast Hierarchical Aggregation Queries for Effective Visual Spatiotemporal Explorations

• DOI: 10.1109/cluster.2019.8891029
• 发表时间: 2019-09
• 期刊: 2019 IEEE International Conference on Cluster Computing (CLUSTER)
• 作者: Saptashwa Mitra;Paahuni Khandelwal;S. Pallickara;S. Pallickara

Enabling Fast Exploratory Analyses Over Voluminous Spatiotemporal Data Using Analytical Engines

使用分析引擎对大量时空数据进行快速探索性分析

• DOI: 10.1109/tbdata.2019.2939834
• 发表时间: 2020
• 期刊: IEEE Transactions on Big Data
• 影响因子: 7.2
• 作者: Rammer, Daniel;Buddhika, Thilina;Malensek, Matthew;Pallickara, Shrideep;Pallickara, Sangmi
• 通讯作者: Pallickara, Sangmi