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: 2034479
• 负责人: Jimeng Sun
• 金额: $ 75.51万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-16 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2034479&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.

时空分析可以使许多发现，包括减少交通拥堵，确定部署移动诊所的热点地区以及城市规划。不幸的是，数据带来了许多计算挑战。 Standard assumptions in machine learning and data mining algorithms are violated by the complex nature of spatio-temporal data. 这些包括观测值的空间和时间相关性，观测值的动态和突然变化，相对于长度和频率的测量可变性以及跨越多个信息来源的多源数据。为了认识到这些挑战，已经采取了各种努力来开发专业的时空模型。然而，迄今为止，这些算法主要设计用于分析中小型数据集。该项目的目的是开发一个全面的计算张量平台，以从广泛的应用程序中从时空数据中执行自动数据驱动的发现。该项目还包括一系列综合的教育活动，例如大规模开放的在线课程，该课程涵盖了计算机科学和地理空间应用的融合，年度时空数据挑战和黑客马拉松的汇合处，并在亚特兰大科学节上进行公众的参与。即使在隐私约束下，也没有牺牲实时性能，计算可扩展性和跨站点学习。所提出的平台将概括张量建模，以涵盖时空数据的复杂性质，包括时间不规则性，时空相关性和不断发展的分布。它将能够从异质来源整合多源数据，以产生强大而有凝聚力的学习模式。新颖的算法还将促进分散环境中的学习，同时保留隐私。计算平台将包含可互换的模块，该模块可以适应新的时空设置并包含其他上下文信息。 随附的算法套件将从大型时空数据中实现预测性学习，模式挖掘和变化检测。 该项目的广泛适用性将在包括城市运输服务，房地产市场交易和人口健康在内的各种数据上证明。引入的算法创新可用于扩展其他机器学习模型。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估标准，被认为值得通过评估来获得支持。

项目成果

LogPar: Logistic PARAFAC2 Factorization for Temporal Binary Data with Missing Values.

• DOI: 10.1145/3394486.3403213
• 发表时间: 2020-08
• 期刊: KDD : proceedings. International Conference on Knowledge Discovery & Data Mining
• 作者: Yin K;Afshar A;Ho JC;Cheung WK;Zhang C;Sun J
• 通讯作者: Sun J

GOCPT: Generalized Online Canonical Polyadic Tensor Factorization and Completion

GOCPT：广义在线正则多元张量分解和完成

• DOI: 10.24963/ijcai.2022/326
• 发表时间: 2022
• 期刊: roceedings of the Thirty-First International Joint Conference on Artificial Intelligence
• 作者: Yang, Chaoqi;Qian, Cheng;Sun, Jimeng
• 通讯作者: Sun, Jimeng

CP Tensor Decomposition with Cannot-Link Intermode Constraints

具有无法链接模间约束的 CP 张量分解

• DOI: 10.1137/1.9781611975673.80
• 发表时间: 2019
• 期刊: SIAM Data mining
• 作者: Jette Henderson†, Bradley A

Multi-version Tensor Completion for Time-delayed Spatio-temporal Data

• DOI: 10.24963/ijcai.2021/400
• 发表时间: 2021-05
• 作者: Cheng Qian;Nikos Kargas;Cao Xiao;Lucas Glass;N. Sidiropoulos;Jimeng Sun

Multi-faceted analysis and prediction for the outbreak of pediatric respiratory syncytial virus

• DOI: 10.1093/jamia/ocad212
• 发表时间: 2023-11-02
• 期刊: JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
• 影响因子: 6.4
• 作者: Yang，Chaoqi;Gao，Junyi;Sun，Jimeng
• 通讯作者: Sun，Jimeng