ATD: Predictive Anomaly Detection for Spatio-Temporal Data with Multidimensional Persistence
ATD:具有多维持久性的时空数据的预测异常检测
基本信息
- 批准号:2220613
- 负责人:
- 金额:$ 10万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-08-01 至 2026-07-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
The focus of this project is pattern mining of valuable information from spatio-temporal (ST) data, which is increasingly available through various geo-positioning techniques and critically important for many real-life applications including human mobility understanding, smart transportation, urban planning, public safety, health care, and environmental management. The presence of dependencies among measurements induced by the spatial and temporal dimensions is the main challenge when dealing with ST data. Most modern methods deal with such problems by splitting spatial and temporal dimensions and adding a merge post-processing step which, in turn, leads to the loss of crucial intertwined knowledge among variables. This project will use state-of-the-art theories from machine learning and mathematical topology to simultaneously include spatial and temporal variables within the mining processes, with application to modeling of large ST datasets. Students will be involved in this cross-disciplinary research, which lies at the interface of mathematics, computer science, and data science. The investigators will develop a novel approach to model spatial and temporal interdependencies within ST data by using the most recent techniques of topological data analysis (TDA). The central goal is establishing a new TDA theory, Multi-Persistence, which will better capture shape evolving patterns in ST data with respect to time, and produce a highly expressive unique topological fingerprint of data without splitting spatial and temporal dimensions. The reduced computational cost of calculating topological summaries will permit development of the two-differentiable objects often needed by machine learning (ML) methods and will increase current capabilities to handle large ST datasets. Additional research activities include investigation of the utility of TDA and the new methodology within the context of threat detection tasks for large ST datasets in several settings, such as threat detection in traffic networks, severity predictions, and wildfire research.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.
该项目的重点是从时空(ST)数据中挖掘有价值信息的模式,这些数据越来越多地通过各种地理定位技术获得,对许多现实生活中的应用至关重要,包括人类移动性理解,智能交通,城市规划,公共安全,医疗保健和环境管理。在处理ST数据时,由空间和时间维度引起的测量之间的依赖性的存在是主要的挑战。大多数现代方法通过分割空间和时间维度并添加合并后处理步骤来处理这些问题,这反过来又导致变量之间关键的交织知识的丢失。该项目将使用机器学习和数学拓扑学的最先进理论,同时在挖掘过程中包括空间和时间变量,并应用于大型ST数据集的建模。 学生将参与这种跨学科的研究,它位于数学,计算机科学和数据科学的接口。研究人员将开发一种新的方法,通过使用最新的拓扑数据分析(TDA)技术来对ST数据中的空间和时间相互依赖性进行建模。其核心目标是建立一个新的TDA理论,多持久性,这将更好地捕捉形状演变模式的ST数据相对于时间,并产生一个高度表达的独特的拓扑指纹的数据没有分裂的空间和时间维。计算拓扑摘要的计算成本降低,将允许开发机器学习(ML)方法经常需要的两个可微对象,并将增加当前处理大型ST数据集的能力。其他研究活动包括调查TDA的实用性和在几种环境中大型ST数据集的威胁检测任务背景下的新方法,例如交通网络中的威胁检测,严重性预测和野火研究。该奖项反映了NSF的法定使命,并被认为值得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来支持。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Baris Coskunuzer其他文献
Non-properly embedded H-planes in $${\mathbb H}^2\times {\mathbb R}$$
- DOI:
10.1007/s00208-017-1550-2 - 发表时间:
2017-05-25 - 期刊:
- 影响因子:1.400
- 作者:
Baris Coskunuzer;William H. Meeks III;Giuseppe Tinaglia - 通讯作者:
Giuseppe Tinaglia
Minimal Surfaces in Hyperbolic 3‐Manifolds
双曲 3 流形中的最小曲面
- DOI:
10.1002/cpa.21961 - 发表时间:
2018 - 期刊:
- 影响因子:3
- 作者:
Baris Coskunuzer - 通讯作者:
Baris Coskunuzer
H-Surfaces with Arbitrary Topology in Hyperbolic 3-Space
- DOI:
10.1007/s12220-016-9715-x - 发表时间:
2016-06 - 期刊:
- 影响因子:0
- 作者:
Baris Coskunuzer - 通讯作者:
Baris Coskunuzer
Number of least area planes in Gromov hyperbolic 3-spaces
格罗莫夫双曲 3 空间中最小面积平面的数量
- DOI:
10.1090/s0002-9939-10-10308-6 - 发表时间:
2008 - 期刊:
- 影响因子:0
- 作者:
Baris Coskunuzer - 通讯作者:
Baris Coskunuzer
Mean convex hulls and least area disks spanning extreme curves
- DOI:
10.1007/s00209-005-0884-8 - 发表时间:
2006-01-26 - 期刊:
- 影响因子:1.000
- 作者:
Baris Coskunuzer - 通讯作者:
Baris Coskunuzer
Baris Coskunuzer的其他文献
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{{ truncateString('Baris Coskunuzer', 18)}}的其他基金
Distribution Network Resilience Enhancement with Topological Neural Networks
利用拓扑神经网络增强配电网弹性
- 批准号:
2229417 - 财政年份:2023
- 资助金额:
$ 10万 - 项目类别:
Standard Grant
Minimal Surfaces in Hyperbolic 3-Manifolds
双曲 3 流形中的最小曲面
- 批准号:
2202584 - 财政年份:2022
- 资助金额:
$ 10万 - 项目类别:
Standard Grant
Algorithms for Modern Power Systems PI Workshop
现代电力系统算法 PI 研讨会
- 批准号:
1841312 - 财政年份:2018
- 资助金额:
$ 10万 - 项目类别:
Continuing Grant
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