Big Data Modeling via Moment-Based Methodologies and the Statistical Analysis of Spatio-Temporal Measurements
通过基于矩的方法进行大数据建模以及时空测量的统计分析
基本信息
- 批准号:RGPIN-2019-06323
- 负责人:
- 金额:$ 1.17万
- 依托单位:
- 依托单位国家:加拿大
- 项目类别:Discovery Grants Program - Individual
- 财政年份:2020
- 资助国家:加拿大
- 起止时间:2020-01-01 至 2021-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Nowadays, multivariate data originating for instance from biostatistics, meteorological, engineering or astronomical studies are becoming more challenging to data mine in light of their increasing complexity and size. Efficient methodologies that are principally based on joint sample moments and are independent of the sample size are advocated in this research proposal as they are ideally suited for analyzing Big Data'. As well, such techniques mitigate the curse of dimensionality. The distributional representations resulting from generalizations of widely utilized models are expressed in functional forms that allow for interpretability, lend themselves to algebraic manipulations and give rise to highly flexible copulae, which describe the dependence between variables of interest. Being remarkably versatile, such models should find applications in reliability theory and quality assurance testing.
The results will be adapted to the context of regression with a view to discarding uninformative variables and eliciting relevant patterns and relationships between the significant ones. As well, both novel and established multivariate methodologies such as hierarchical clustering analysis and data visualization techniques such as scatterplot matrices will be brought to bear to great advantage in the fields of neuroimaging - for assessing the dissimilarities between vectors of responses associated with certain stimuli - and environmetrics - for detecting trends in the face of climatic changes. As well, they should enhance the understanding of the underlying processes and, for instance, lead to advances in predictive analytics in connection with the occurrence of catastrophic events such as floods and earthquakes. The software documentation and source code to be developed for implementing the planned distributional breakthroughs shall be made available.
Various approaches will be devised to extract pertinent distributional information from relatively small subsets of large-scale data sets. Once utilized in conjunction with innovative data reduction and variable selection techniques, the modeling methodologies being herein advocated will permit to process more rapidly massive spatio-temporal and higher-dimensional data sets that frequently arrive in streams as in the cases of high throughput cancer screening and DNA sequencing, the burgeoning blockchain technologies, metadata analyses, and the fast expanding field of artificial intelligence, which is at the core of autonomous and interactive systems such as self-driving vehicles.
By addressing both volume and velocity in connection with the analysis of massive and complex streaming data, the proposed generalized models and innovative moment-based methodologies herald a paradigmatic shift in the processing of large-scale multivariate observations.
如今,源自例如生物统计学、气象学、工程学或天文学研究的多变量数据由于其日益增加的复杂性和规模而变得对数据挖掘更具挑战性。有效的方法,主要是基于联合样本矩和独立的样本大小,提倡在这个研究建议,因为它们非常适合分析大数据。同样,这些技术减轻了维度灾难。分布表示广泛使用的模型的概括所产生的功能的形式,允许解释性,借给自己的代数操作,并产生高度灵活的copulae,它描述了相关变量之间的依赖关系。作为显着的通用性,这样的模型应该在可靠性理论和质量保证测试中找到应用。
结果将根据回归的情况加以调整,以排除无意义的变量,并得出相关的模式和重要变量之间的关系。此外,新的和建立的多变量方法,如层次聚类分析和数据可视化技术,如散点图矩阵,将带来承担在神经成像领域的巨大优势-用于评估与某些刺激相关的响应向量之间的差异-和气候变化指标-用于检测在面对气候变化的趋势。此外,它们还应该加强对潜在过程的理解,例如,导致与洪水和地震等灾难性事件发生有关的预测分析的进步。应提供为实现计划的分布式突破而开发的软件文档和源代码。
将设计各种方法,从大规模数据集的相对较小的子集中提取相关的分布信息。一旦与创新的数据缩减和变量选择技术结合使用,本文所倡导的建模方法将允许更快速地处理大量时空和高维数据集,这些数据集经常以流的形式到达,如高通量癌症筛查和DNA测序、新兴的区块链技术、元数据分析和快速扩展的人工智能领域。这是自动驾驶汽车等自主和交互系统的核心。
通过解决大量和复杂的流数据的分析,所提出的广义模型和创新的基于矩的方法,预示着在处理大规模的多变量观测的范式转变。
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)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Provost, Serge其他文献
Provost, Serge的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Provost, Serge', 18)}}的其他基金
Big Data Modeling via Moment-Based Methodologies and the Statistical Analysis of Spatio-Temporal Measurements
通过基于矩的方法进行大数据建模以及时空测量的统计分析
- 批准号:
RGPIN-2019-06323 - 财政年份:2022
- 资助金额:
$ 1.17万 - 项目类别:
Discovery Grants Program - Individual
Big Data Modeling via Moment-Based Methodologies and the Statistical Analysis of Spatio-Temporal Measurements
通过基于矩的方法进行大数据建模以及时空测量的统计分析
- 批准号:
RGPIN-2019-06323 - 财政年份:2021
- 资助金额:
$ 1.17万 - 项目类别:
Discovery Grants Program - Individual
Big Data Modeling via Moment-Based Methodologies and the Statistical Analysis of Spatio-Temporal Measurements
通过基于矩的方法进行大数据建模以及时空测量的统计分析
- 批准号:
RGPIN-2019-06323 - 财政年份:2019
- 资助金额:
$ 1.17万 - 项目类别:
Discovery Grants Program - Individual
Methodologies for Modeling and Analyzing Massive Environmental and Biomedical Data Sets
大量环境和生物医学数据集的建模和分析方法
- 批准号:
RGPIN-2014-05193 - 财政年份:2018
- 资助金额:
$ 1.17万 - 项目类别:
Discovery Grants Program - Individual
Methodologies for Modeling and Analyzing Massive Environmental and Biomedical Data Sets
大量环境和生物医学数据集的建模和分析方法
- 批准号:
RGPIN-2014-05193 - 财政年份:2017
- 资助金额:
$ 1.17万 - 项目类别:
Discovery Grants Program - Individual
Methodologies for Modeling and Analyzing Massive Environmental and Biomedical Data Sets
大量环境和生物医学数据集的建模和分析方法
- 批准号:
RGPIN-2014-05193 - 财政年份:2016
- 资助金额:
$ 1.17万 - 项目类别:
Discovery Grants Program - Individual
Methodologies for Modeling and Analyzing Massive Environmental and Biomedical Data Sets
大量环境和生物医学数据集的建模和分析方法
- 批准号:
RGPIN-2014-05193 - 财政年份:2015
- 资助金额:
$ 1.17万 - 项目类别:
Discovery Grants Program - Individual
Methodologies for Modeling and Analyzing Massive Environmental and Biomedical Data Sets
大量环境和生物医学数据集的建模和分析方法
- 批准号:
RGPIN-2014-05193 - 财政年份:2014
- 资助金额:
$ 1.17万 - 项目类别:
Discovery Grants Program - Individual
Advances in distribution theory with applications to transportation logistics and statiscal genesis
分配理论的进展及其在运输物流和统计生成中的应用
- 批准号:
8666-2009 - 财政年份:2013
- 资助金额:
$ 1.17万 - 项目类别:
Discovery Grants Program - Individual
Advances in distribution theory with applications to transportation logistics and statiscal genesis
分配理论的进展及其在运输物流和统计生成中的应用
- 批准号:
8666-2009 - 财政年份:2012
- 资助金额:
$ 1.17万 - 项目类别:
Discovery Grants Program - Individual
相似国自然基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
- 批准号:
- 批准年份:2024
- 资助金额:万元
- 项目类别:合作创新研究团队
Data-driven Recommendation System Construction of an Online Medical Platform Based on the Fusion of Information
- 批准号:
- 批准年份:2024
- 资助金额:万元
- 项目类别:外国青年学者研究基金项目
Development of a Linear Stochastic Model for Wind Field Reconstruction from Limited Measurement Data
- 批准号:
- 批准年份:2020
- 资助金额:40 万元
- 项目类别:
基于Linked Open Data的Web服务语义互操作关键技术
- 批准号:61373035
- 批准年份:2013
- 资助金额:77.0 万元
- 项目类别:面上项目
Molecular Interaction Reconstruction of Rheumatoid Arthritis Therapies Using Clinical Data
- 批准号:31070748
- 批准年份:2010
- 资助金额:34.0 万元
- 项目类别:面上项目
高维数据的函数型数据(functional data)分析方法
- 批准号:11001084
- 批准年份:2010
- 资助金额:16.0 万元
- 项目类别:青年科学基金项目
染色体复制负调控因子datA在细胞周期中的作用
- 批准号:31060015
- 批准年份:2010
- 资助金额:25.0 万元
- 项目类别:地区科学基金项目
Computational Methods for Analyzing Toponome Data
- 批准号:60601030
- 批准年份:2006
- 资助金额:17.0 万元
- 项目类别:青年科学基金项目
相似海外基金
Modeling the Impact of Emerging Mobility Services Using Big Data Analytics
使用大数据分析对新兴移动服务的影响进行建模
- 批准号:
DGECR-2022-00470 - 财政年份:2022
- 资助金额:
$ 1.17万 - 项目类别:
Discovery Launch Supplement
Mining, Fusion and Modeling of Truck Big Data for the development of Agent-Based Microsimulation Models
卡车大数据的挖掘、融合和建模,用于开发基于代理的微观仿真模型
- 批准号:
RGPIN-2017-05843 - 财政年份:2022
- 资助金额:
$ 1.17万 - 项目类别:
Discovery Grants Program - Individual
Harnessing AI-powered big data techniques for 3D plant architecture phenotyping and growth pattern modeling
利用人工智能驱动的大数据技术进行 3D 植物结构表型分析和生长模式建模
- 批准号:
578508-2022 - 财政年份:2022
- 资助金额:
$ 1.17万 - 项目类别:
Alliance Grants
Modeling the Impact of Emerging Mobility Services Using Big Data Analytics
使用大数据分析对新兴移动服务的影响进行建模
- 批准号:
RGPIN-2022-03037 - 财政年份:2022
- 资助金额:
$ 1.17万 - 项目类别:
Discovery Grants Program - Individual
Big Data Modeling via Moment-Based Methodologies and the Statistical Analysis of Spatio-Temporal Measurements
通过基于矩的方法进行大数据建模以及时空测量的统计分析
- 批准号:
RGPIN-2019-06323 - 财政年份:2022
- 资助金额:
$ 1.17万 - 项目类别:
Discovery Grants Program - Individual
Machine learning, big data, and simulation modeling to lower the burden of cervical cancer
机器学习、大数据和仿真建模减轻宫颈癌负担
- 批准号:
574931-2022 - 财政年份:2022
- 资助金额:
$ 1.17万 - 项目类别:
University Undergraduate Student Research Awards
Bayesian Modeling and Scalable Inference for Big Data Streams
大数据流的贝叶斯建模和可扩展推理
- 批准号:
RGPIN-2019-03962 - 财政年份:2022
- 资助金额:
$ 1.17万 - 项目类别:
Discovery Grants Program - Individual
Robust System Modeling, Process Monitoring and Fault Diagnosis in the Era of Big Data
大数据时代的鲁棒系统建模、过程监控和故障诊断
- 批准号:
RGPIN-2020-04138 - 财政年份:2022
- 资助金额:
$ 1.17万 - 项目类别:
Discovery Grants Program - Individual
Assessment and Visualization of Regional Climate Risks through High-Resolution Ensemble Modeling and Big Data Analytics
通过高分辨率集合建模和大数据分析评估和可视化区域气候风险
- 批准号:
RGPIN-2019-03966 - 财政年份:2022
- 资助金额:
$ 1.17万 - 项目类别:
Discovery Grants Program - Individual
Foundation of macroscopic human flow modeling around big cities based on GPS data
基于GPS数据的大城市周边宏观人流建模基础
- 批准号:
22H01711 - 财政年份:2022
- 资助金额:
$ 1.17万 - 项目类别:
Grant-in-Aid for Scientific Research (B)