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BD Spokes: SPOKE: WEST: Collaborative: MetroInsight: Knowledge Discovery and Real-Time Interventions from Sensory Data Flows in Urban Spaces

BD 发言：发言：WEST：协作：MetroInsight：城市空间中感知数据流的知识发现和实时干预

基本信息

批准号：
1636916
负责人：
Mani Srivastava
金额：
$ 30.31万
依托单位：
University of California-Los Angeles
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 2019-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1636916&HistoricalAwards=false
关键词：
BD Spokes SPOKE WEST Collaborative

项目摘要

The MetroInsight project is building an end to end system for knowledge discovery from real-time data streams collected through a variety of sensors, data collection and aggregation methods. These data streams are highly dimensional with multiple sensors observing same or similar phenomena over multiple sensory spectrums and scales. These are also sometimes real-time and/or have strong timing relationships that is necessary to support metropolitan infrastructure through effective analytics and policy support. The project brings together a diverse number of partners utilities, universities, companies and cities with the ability to contribute novel tools and urban sensor data and to translate knowledge into actions. MetroInsight's unique combination of tools, data and partnerships, in part with the MetroLab Network , makes it well poised to set an example for the MetroLab programs across the nation as well as the rest of the municipal governments. The project will explore connections between multimodal datasets and urban infrastructure management to build a practical system consisting of integrated tools, as well as training a new generation of metropolitan workforce. As part of an ambitious plan for community building and workforce development, the project includes creation of new learning modules, certification programs on energy and sustainability, an online courses on sensor data analytics and new capstone projects in a new Data Science master's degree program.To achieve project goals, MetroInsight is building infrastructure for managing data, networks and processing that will support design of new algorithms and tools in the project. Specifically, the project is developing algorithms to transform multimodal urban data to a lower dimensional data that reflects underlying physical and social phenomena. These low dimensional data may consist of population level data suitable for dynamic processing to support real time monitoring and visualization by cityscale operators of various lifelines from transportation, communications to emergency response. To address technical challenges in complex and subtle spatiotemporal dynamics of interdependent urban networks, MetroInsight will develop metadata methods and tools that support discovery of operational interdependencies, quantification of uncertainties for decision support and to provide assurances related to integrity and security of data, compliance related to ethical and legal privacy expectations.

MetroInsight 项目正在构建一个端到端系统，用于从通过各种传感器、数据收集和聚合方法收集的实时数据流中发现知识。这些数据流是高维度的，多个传感器在多个感官频谱和尺度上观察相同或相似的现象。这些有时也是实时的和/或具有强大的时序关系，这是通过有效的分析和政策支持来支持大都市基础设施所必需的。该项目汇集了公用事业、大学、公司和城市等众多合作伙伴，有能力贡献新颖的工具和城市传感器数据，并将知识转化为行动。 MetroInsight 独特的工具、数据和合作伙伴关系（部分与 MetroLab Network 相结合）使其能够为全国以及其他市政府的 MetroLab 计划树立榜样。该项目将探索多模式数据集和城市基础设施管理之间的联系，以建立一个由集成工具组成的实用系统，并培训新一代的城市劳动力。作为社区建设和劳动力发展雄心勃勃的计划的一部分，该项目包括创建新的学习模块、能源和可持续发展认证计划、传感器数据分析在线课程以及新数据科学硕士学位课程中的新顶点项目。为了实现项目目标，MetroInsight 正在构建用于管理数据、网络和处理的基础设施，以支持项目中新算法和工具的设计。具体来说，该项目正在开发算法，将多模式城市数据转换为反映潜在物理和社会现象的低维数据。这些低维数据可能包括适合动态处理的人口水平数据，以支持城市规模运营商对从交通、通信到应急响应等各种生命线的实时监控和可视化。为了解决相互依赖的城市网络复杂而微妙的时空动态中的技术挑战，MetroInsight 将开发元数据方法和工具，支持发现操作相互依赖性、量化决策支持的不确定性，并提供与数据完整性和安全性相关的保证、与道德和法律隐私期望相关的合规性。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

In-database Distributed Machine Learning: Demonstration using Teradata SQL Engine

DOI：
10.14778/3352063.3352083
发表时间：
2019-08-01
期刊：
PROCEEDINGS OF THE VLDB ENDOWMENT
影响因子：
2.5
作者：
Sandha, Sandeep Singh;Cabrera, Wellington;Srivastava, Mani
通讯作者：
Srivastava, Mani

Enabling Edge Devices that Learn from Each Other: Cross Modal Training for Activity Recognition

DOI：
10.1145/3213344.3213351
发表时间：
2018-06
期刊：
Proceedings of the 1st International Workshop on Edge Systems, Analytics and Networking
影响因子：
0
作者：
Tianwei Xing;S. Sandha;Bharathan Balaji;Supriyo Chakraborty;M. Srivastava
通讯作者：
Tianwei Xing;S. Sandha;Bharathan Balaji;Supriyo Chakraborty;M. Srivastava

Data Hub Architecture for Smart Cities

智慧城市的数据中心架构

DOI：
10.1145/3131672.3137001
发表时间：
2017
期刊：
Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems
影响因子：
0
作者：
Koh, Jason;Sandha, Sandeep;Balaji, Bharathan;Crawl, Daniel;Altintas, Ilkay;Gupta, Rajesh;Srivastava, Mani
通讯作者：
Srivastava, Mani

Enabling Privacy Policies for mHealth Studies

为移动医疗研究启用隐私政策