Temporal Causal Pattern Mining From Heterogeneous Event Sequences for Predictive Analytics
从异构事件序列中挖掘时间因果模式以进行预测分析
基本信息
- 批准号:RGPIN-2018-04403
- 负责人:
- 金额:$ 1.68万
- 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
The Internet continuously produces large flows of data and forms events sequences that convey knowledge about individuals' profiles, behaviors, communities, opinions, influences, intentions, and trends. Similarly, the increasing number of Internet of Things (IoT) sensors generate a continuous flow of large amounts of data chunks that are usually time-stamped and possibly geo-stamped, and form complex heterogeneous multidimensional event sequences conveying also a great deal of knowledge about individuals' physical activities, mobility patterns, environmental changes, and medical information. Extracting interesting patterns from these complex sequence data for predictive analytics is an important and challenging problem.
Mining temporal causal patterns from massive and complex heterogeneous sequence data for predictive analytics is the main purpose of this program. The data types addressed are multisource, characterized by heterogeneity, very large volume, significant noise and missing values, high-dimensional and strong interrelations between their attributes.
The long-term goal of this research program is to develop theories to better understand and predict normal and abnormal behaviors through mining massive sequence data. Through this process, our goal is also the development of general theories that can guide the design of efficient sequence analysis methods for real-world applications.
This program will be carried out by accomplishing a number of interrelated projects. 1) Develop a novel mathematical framework for causality discovery from event sequences. Deep learning models such as deep Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) models combined with probabilistic causality models will be investigated to discover statistically significant patterns with causal relationships. 2) Develop efficient algorithms for mining heterogeneity of event sequences. We will create new latent representations of data that serve as abstractions by projecting data into new spaces, which will make learning easier. The algorithms developed here will be able to extract complex causal patterns and interactions between various event sequences. 3) Develop efficient algorithms for predicting violent behaviors in social networks. This project is an important application of the theory we developed in this research program to solve a practical real world problem, and would have the potential to generate a beneficial social impact for social network users. It will also serve as a testbed of this research program.
互联网不断产生大量的数据流,并形成事件序列,这些事件序列传达了关于个人概况、行为、社区、意见、影响、意图和趋势的知识。类似地,越来越多的物联网(IoT)传感器生成大量数据块的连续流,这些数据块通常是有时间戳的并且可能是有地理戳的,并且形成复杂的异构多维事件序列,这些事件序列还传达关于个人的身体活动、移动模式、环境变化和医疗信息的大量知识。从这些复杂的序列数据中提取有趣的模式进行预测分析是一个重要且具有挑战性的问题。
从大量复杂的异构序列数据中挖掘时间因果模式进行预测分析是该计划的主要目的。处理的数据类型是多尺度的,其特征是异质性,非常大的体积,显着的噪音和缺失值,高维和它们的属性之间的强相互关系。
该研究计划的长期目标是通过挖掘大量序列数据来开发理论,以更好地理解和预测正常和异常行为。通过这个过程,我们的目标也是一般理论的发展,可以指导设计有效的序列分析方法,为现实世界的应用。
该计划将通过完成一些相互关联的项目来实施。1)开发一个新的数学框架,从事件序列中发现因果关系。深度学习模型,如深度卷积神经网络(CNN)和长短期记忆(LSTM)模型与概率因果关系模型相结合,将被研究,以发现具有因果关系的统计学显著模式。2)开发有效的算法来挖掘事件序列的异质性。我们将通过将数据投影到新的空间中来创建新的数据潜在表示,作为抽象,这将使学习更容易。这里开发的算法将能够提取复杂的因果模式和各种事件序列之间的相互作用。3)开发预测社交网络中暴力行为的有效算法。这个项目是我们在这个研究项目中开发的理论的一个重要应用,以解决一个实际的真实的世界问题,并有可能为社交网络用户产生有益的社会影响。它也将作为这项研究计划的试验平台。
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 }}
Chikhaoui, Belkacem其他文献
Clustering home activity distributions for automatic detection of mild cognitive impairment in older adults
- DOI:
10.3233/ais-160385 - 发表时间:
2016-01-01 - 期刊:
- 影响因子:1.7
- 作者:
Akl, Ahmad;Chikhaoui, Belkacem;Mihailidis, Alex - 通讯作者:
Mihailidis, Alex
Aggressive and agitated behavior recognition from accelerometer data using non-negative matrix factorization
- DOI:
10.1007/s12652-017-0537-x - 发表时间:
2018-10-01 - 期刊:
- 影响因子:0
- 作者:
Chikhaoui, Belkacem;Ye, Bing;Mihailidis, Alex - 通讯作者:
Mihailidis, Alex
A standard ontology for smart spaces
- DOI:
10.1504/ijwgs.2010.035091 - 发表时间:
2010-01-01 - 期刊:
- 影响因子:1
- 作者:
Abdulrazak, Bessam;Chikhaoui, Belkacem;Fraikin, Benoit - 通讯作者:
Fraikin, Benoit
Machine Learning Based Classification Using Clinical and DaTSCAN SPECT Imaging Features: A Study on Parkinson's Disease and SWEDD
- DOI:
10.1109/trpms.2018.2877754 - 发表时间:
2019-03-01 - 期刊:
- 影响因子:4.4
- 作者:
Mabrouk, Rostom;Chikhaoui, Belkacem;Bentabet, Layachi - 通讯作者:
Bentabet, Layachi
COVID-19: Detecting depression signals during stay-at-home period.
- DOI:
10.1177/14604582221094931 - 发表时间:
2022-04 - 期刊:
- 影响因子:3
- 作者:
Tshimula, Jean Marie;Chikhaoui, Belkacem;Wang, Shengrui - 通讯作者:
Wang, Shengrui
Chikhaoui, Belkacem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('Chikhaoui, Belkacem', 18)}}的其他基金
Temporal Causal Pattern Mining From Heterogeneous Event Sequences for Predictive Analytics
从异构事件序列中挖掘时间因果模式以进行预测分析
- 批准号:
RGPIN-2018-04403 - 财政年份:2022
- 资助金额:
$ 1.68万 - 项目类别:
Discovery Grants Program - Individual
Développement d'une plateforme innovante basée sur les compétences et l'intelligence artificielle pour soutenir la gestion des talents
能力与智能技术创新平台开发,促进人才管理
- 批准号:
566466-2021 - 财政年份:2021
- 资助金额:
$ 1.68万 - 项目类别:
Alliance Grants
Temporal Causal Pattern Mining From Heterogeneous Event Sequences for Predictive Analytics
从异构事件序列中挖掘时间因果模式以进行预测分析
- 批准号:
RGPIN-2018-04403 - 财政年份:2021
- 资助金额:
$ 1.68万 - 项目类别:
Discovery Grants Program - Individual
Temporal Causal Pattern Mining From Heterogeneous Event Sequences for Predictive Analytics
从异构事件序列中挖掘时间因果模式以进行预测分析
- 批准号:
RGPIN-2018-04403 - 财政年份:2019
- 资助金额:
$ 1.68万 - 项目类别:
Discovery Grants Program - Individual
Temporal Causal Pattern Mining From Heterogeneous Event Sequences for Predictive Analytics
从异构事件序列中挖掘时间因果模式以进行预测分析
- 批准号:
RGPIN-2018-04403 - 财政年份:2018
- 资助金额:
$ 1.68万 - 项目类别:
Discovery Grants Program - Individual
Temporal Causal Pattern Mining From Heterogeneous Event Sequences for Predictive Analytics
从异构事件序列中挖掘时间因果模式以进行预测分析
- 批准号:
DGECR-2018-00346 - 财政年份:2018
- 资助金额:
$ 1.68万 - 项目类别:
Discovery Launch Supplement
An approach for detecting behavioral aspects of older adults with dementia using patterns of motion
一种利用运动模式检测患有痴呆症的老年人行为方面的方法
- 批准号:
438628-2013 - 财政年份:2015
- 资助金额:
$ 1.68万 - 项目类别:
Postdoctoral Fellowships
An approach for detecting behavioral aspects of older adults with dementia using patterns of motion
一种利用运动模式检测患有痴呆症的老年人行为方面的方法
- 批准号:
438628-2013 - 财政年份:2014
- 资助金额:
$ 1.68万 - 项目类别:
Postdoctoral Fellowships
An approach for detecting behavioral aspects of older adults with dementia using patterns of motion
一种利用运动模式检测患有痴呆症的老年人行为方面的方法
- 批准号:
438628-2013 - 财政年份:2013
- 资助金额:
$ 1.68万 - 项目类别:
Postdoctoral Fellowships
Une approche basée sur l'analyse des séquences pour la détection et l'identification des comportements
行为检测和识别序列分析的基本方法
- 批准号:
408264-2011 - 财政年份:2012
- 资助金额:
$ 1.68万 - 项目类别:
Alexander Graham Bell Canada Graduate Scholarships - Doctoral
相似海外基金
Temporal Causal Pattern Mining From Heterogeneous Event Sequences for Predictive Analytics
从异构事件序列中挖掘时间因果模式以进行预测分析
- 批准号:
RGPIN-2018-04403 - 财政年份:2022
- 资助金额:
$ 1.68万 - 项目类别:
Discovery Grants Program - Individual
Temporal Causal Pattern Mining From Heterogeneous Event Sequences for Predictive Analytics
从异构事件序列中挖掘时间因果模式以进行预测分析
- 批准号:
RGPIN-2018-04403 - 财政年份:2021
- 资助金额:
$ 1.68万 - 项目类别:
Discovery Grants Program - Individual
A causal test of the hippocampal circuits mediating pattern completion
海马回路介导模式完成的因果测试
- 批准号:
557125-2020 - 财政年份:2021
- 资助金额:
$ 1.68万 - 项目类别:
Banting Postdoctoral Fellowships Tri-council
A causal test of the functional impact of adult neurogenesis on hippocampal pattern separation
成人神经发生对海马模式分离功能影响的因果检验
- 批准号:
455017 - 财政年份:2021
- 资助金额:
$ 1.68万 - 项目类别:
Fellowship Programs
A causal test of the hippocampal circuits mediating pattern completion
海马回路介导模式完成的因果测试
- 批准号:
557125-2020 - 财政年份:2020
- 资助金额:
$ 1.68万 - 项目类别:
Banting Postdoctoral Fellowships Tri-council
Temporal Causal Pattern Mining From Heterogeneous Event Sequences for Predictive Analytics
从异构事件序列中挖掘时间因果模式以进行预测分析
- 批准号:
RGPIN-2018-04403 - 财政年份:2019
- 资助金额:
$ 1.68万 - 项目类别:
Discovery Grants Program - Individual
Temporal Causal Pattern Mining From Heterogeneous Event Sequences for Predictive Analytics
从异构事件序列中挖掘时间因果模式以进行预测分析
- 批准号:
RGPIN-2018-04403 - 财政年份:2018
- 资助金额:
$ 1.68万 - 项目类别:
Discovery Grants Program - Individual
Temporal Causal Pattern Mining From Heterogeneous Event Sequences for Predictive Analytics
从异构事件序列中挖掘时间因果模式以进行预测分析
- 批准号:
DGECR-2018-00346 - 财政年份:2018
- 资助金额:
$ 1.68万 - 项目类别:
Discovery Launch Supplement
CAUSAL ANALYSIS OF AES--PATTERN RECOGNITION SOFTWARE
AES--模式识别软件的因果分析
- 批准号:
2637045 - 财政年份:1997
- 资助金额:
$ 1.68万 - 项目类别:
Global diversification of termite-Its pattern and causal mechanism
白蚁的全球多样化——其模式和因果机制
- 批准号:
07044193 - 财政年份:1995
- 资助金额:
$ 1.68万 - 项目类别:
Grant-in-Aid for international Scientific Research