BigData:IA:Collaborative Research: TIMES: A tensor factorization platform for spatio-temporal data

BigData：IA：协作研究：TIMES：时空数据张量分解平台

• 批准号: 1838042
• 负责人: Jimeng Sun
• 金额: $ 77.35万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1838042&HistoricalAwards=false
• 关键词: BigData; IA; Collaborative; Research; TIMES

Spatio-temporal analyses can enable many discoveries including reducing traffic congestion, identifying hotspot areas to deploy mobile clinics, and urban planning. Unfortunately, the data poses many computational challenges. Standard assumptions in machine learning and data mining algorithms are violated by the complex nature of spatio-temporal data. These include spatial and temporal correlation of observations, dynamic and abrupt changes in observations, variability in measurements with respect to length and frequency, and multi-sourced data that spans multiple sources of information. In recognition of these challenges, various efforts have been undertaken to develop specialized spatiotemporal models. Yet, to date, these algorithms are predominately designed to analyze small- to medium-sized datasets. The goal of this project is to develop a comprehensive computational tensor platform to perform automated, data-driven discovery from spatio-temporal data across a broad range of applications. The project also includes a set of integrated educational activities such as a Massive Open Online Course that covers cross-disciplinary topics at the confluence of computer science and geospatial applications, annual spatio-temporal data challenges and hackathons, and an annual event at the Atlanta Science Festival to create public awareness and encourage participation by women and minorities.The project will contain algorithmic innovations that reflect appropriate assumptions of spatio-temporal data without sacrificing real-time performance, computational scalability, and cross-site learning even under privacy constraints. The proposed platform will generalize tensor modeling to encompass the complex nature of spatio-temporal data including time irregularity, spatiotemporal correlations, and evolving distributions. It will enable the integration of multi-sourced data from heterogeneous sources to yield robust and cohesive learned patterns. The novel algorithms will also facilitate learning in decentralized settings while preserving privacy. The computational platform will contain interchangeable modules that can adapt to new spatio-temporal settings and incorporate additional contextual information. The accompanying suite of algorithms will enable predictive learning, pattern mining, and change detection from large-sized spatio-temporal data. The broad applicability of the project will be demonstrated on a diverse range of data including urban transportation services, real estate market transactions, and population health. The algorithmic innovations introduced can be used to scale other machine learning models.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.

时空分析可以实现许多发现，包括减少交通拥堵、识别热点区域以部署移动的诊所和城市规划。不幸的是，这些数据带来了许多计算挑战。 时空数据的复杂性违背了机器学习和数据挖掘算法中的标准假设。 其中包括观测的空间和时间相关性、观测的动态和突然变化、长度和频率方面的测量变异性以及跨越多个信息源的多源数据。认识到这些挑战，已作出各种努力，开发专门的时空模型。然而，到目前为止，这些算法主要用于分析中小型数据集。该项目的目标是开发一个全面的计算张量平台，以在广泛的应用程序中从时空数据中执行自动化，数据驱动的发现。该项目还包括一系列综合教育活动，如大规模开放式在线课程，涵盖计算机科学和地理空间应用融合的跨学科主题，年度时空数据挑战和黑客马拉松，以及亚特兰大科学节的年度活动，以提高公众意识，鼓励妇女和少数民族参与。该项目将包含算法创新，在不牺牲实时性能、计算可扩展性和跨站点学习的情况下，即使在隐私约束下，也可以实现时空数据的假设。该平台将推广张量建模，以涵盖时空数据的复杂性质，包括时间不规则性，时空相关性和不断变化的分布。它将使来自不同来源的多源数据的集成产生强大的和有凝聚力的学习模式。新算法还将促进分散式环境中的学习，同时保护隐私。计算平台将包含可互换的模块，这些模块可以适应新的时空设置，并纳入额外的背景信息。 伴随的算法套件将实现预测学习，模式挖掘和大型时空数据的变化检测。 该项目的广泛适用性将在包括城市交通服务、真实的房地产市场交易和人口健康在内的各种数据上得到证明。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

MTC: Multiresolution Tensor Completion from Partial and Coarse Observations

MTC：部分和粗略观测的多分辨率张量补全

• DOI: 10.1145/3447548.3467261
• 发表时间: 2021
• 期刊: KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
• 作者: Yang, Chaoqi;Singh, Navjot;Xiao, Cao;Qian, Cheng;Solomonik, Edgar;Sun, Jimeng
• 通讯作者: Sun, Jimeng