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

• 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

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.

Metroronionsight项目正在建立一个端到端系统，以从通过各种传感器，数据收集和聚合方法收集的实时数据流进行知识发现。 这些数据流是高度尺寸的，多个传感器在多个感觉谱和尺度上观察到相同或相似的现象。这些有时也是实时的和/或具有牢固的时机关系，这对于通过有效的分析和政策支持来支持大都会基础架构是必要的。该项目汇集了许多合作伙伴公用事业，大学，公司和城市，具有贡献新颖的工具和城市传感器数据并将知识转化为行动的能力。 Metroyionsight与Metrolab网络的一部分工具，数据和合作伙伴关系的独特组合使其有望为全国各地的Metrolab计划以及其他市政府设定榜样。该项目将探索多模式数据集与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，并培训新一代的大都市劳动力。作为一个雄心勃勃的社区建设和劳动力发展计划的一部分，该项目包括创建新的学习模块，有关能源和可持续性的认证计划，在新的数据科学硕士学位计划中有关传感器数据分析的在线课程和新的Capstone项目。为了实现项目目标，Metrorosight是为了管理数据，网络和加工设计的基础架构，以支持新的ALGORITH和GRACTION of NEW ALGORITH和GRACTION of NEW ALGORITH和GRATION INSTORMITS的工具。具体而言，该项目正在开发算法将多模式城市数据转换为反映基本物理和社会现象的较低维数据。这些低维数据可能包括适合动态处理的人群级别数据，以支持各种寿命从运输，通信到紧急响应的各种寿命的实时监视和可视化。为了解决相互依存的城市网络复杂而微妙的时空动态的技术挑战，Metro Insight将开发元数据方法和工具，以支持发现操作相互依存的发现，对决策支持的不确定性的量化并提供与数据完整性和数据相关的保证，与数据，合规性，与道德和合法的保密相关。

项目成果

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
• 作者: Tianwei Xing;S. Sandha;Bharathan Balaji;Supriyo Chakraborty;M. Srivastava

Brick : Metadata schema for portable smart building applications

• DOI: 10.1016/j.apenergy.2018.02.091
• 发表时间: 2018-09-15
• 期刊: APPLIED ENERGY
• 影响因子: 11.2
• 作者: Balaji, Bharathan;Bhattacharya, Arka;Whitehouse, Kamin
• 通讯作者: Whitehouse, Kamin

Enabling Privacy Policies for mHealth Studies

为移动医疗研究启用隐私政策

• DOI: 10.1109/bigdata47090.2019.9006338
• 发表时间: 2019
• 期刊: 2019 IEEE International Conference on Big Data (Big Data
• 作者: Wang, Brian;Srivastava, Mani B.
• 通讯作者: Srivastava, Mani B.

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

Deep Convolutional Bidirectional LSTM Based Transportation Mode Recognition

• DOI: 10.1145/3267305.3267529
• 发表时间: 2018-01-01
• 期刊: PROCEEDINGS OF THE 2018 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING AND PROCEEDINGS OF THE 2018 ACM INTERNATIONAL SYMPOSIUM ON WEARABLE COMPUTERS (UBICOMP/ISWC'18 ADJUNCT)
• 作者: Jeyakumar, Jeya Vikranth;Sun Lee, Eun;Srivastava, Mani
• 通讯作者: Srivastava, Mani