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

时空分析可以实现许多发现，包括减少交通拥堵，识别热点地区以部署流动诊所，以及城市规划。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反了机器学习和数据挖掘算法中的标准假设。这些包括观测的空间和时间相关性、观测的动态和突然变化、测量相对于长度和频率的可变性，以及跨越多个信息源的多源数据。认识到这些挑战，已作出各种努力来开发专门的时空模型。然而，到目前为止，这些算法主要是为分析中小型数据集而设计的。该项目的目标是开发一个全面的计算张量平台，以从广泛的应用程序中的时空数据执行自动化的、数据驱动的发现。该项目还包括一系列综合教育活动，如涵盖计算机科学和地理空间应用程序交汇处的跨学科主题的大型开放式在线课程、年度时空数据挑战和黑客松，以及亚特兰大科学节的年度活动，以提高公众意识并鼓励妇女和少数群体参与。该项目将包含算法创新，反映对时空数据的适当假设，而不会牺牲实时性能、计算可伸缩性和即使在隐私限制下的跨站点学习。拟议的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、时空相关性和演变分布。它将能够整合来自不同来源的多源数据，以产生健壮和连贯的学习模式。这些新颖的算法还将促进在分散设置下的学习，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。随之而来的一套算法将支持从大型时空数据进行预测性学习、模式挖掘和变化检测。该项目的广泛适用性将在包括城市交通服务、房地产市场交易和人口健康在内的各种数据上得到展示。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的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.