Grant: Collaborative Research and Development Grants
Queen's University
$63,000.00
Learning Distributed Patterns from Multimodal Streaming Data_x000d_ _x000d_ _x000d_ _x000d_
FY 2020-21
Summary
Human cognition depends on learning, correlating and recognizing data patterns using the sensory system, storing these patterns efficiently and matching them against newly perceived data for recognition and decision making. Pattern recognition has been explored for several decades but new challenges have evolved as thousands of connected digital devices have started generating terabytes of streaming data of different modalities such as audio, video, signal, and text. This data must be ingested, processed and correlated in parallel in real time for advanced digital perception and recognition to create machine intelligence. Large companies and internet cloud service providers are facing a serious challenge today to handle the data explosion problem. We need a system that can learn useful information from multiple types of streaming data in parallel for advanced cognition, store the knowledge, and reproduce the data streams as necessary while discarding unimportant data to optimize storage. The proposed research will develop a real time streaming data processing framework with novel deep learning models to learn and correlate distributed data patterns to enable machine cognition for a patient monitoring application. The novelty of the proposed research lies in 1) developing deep learning models and streaming data processing frameworks to extract regular, irregular and abnormal patterns from multimodal IoT, text and video data while addressing changes in learned patterns known as concept drifts, 2) creating a universal pattern representation and profiling technology to store data patterns as data profiles in a periodic event log rather than massive raw data, thus optimizing storage consumption and facilitating the development of machine learning models to reproduce data streams from the stored profiles, and 3) developing a pattern association model to correlate IoT, text and video data patterns for improved machine cognition. The proposed technology will serve as the backbone for developing a variety of intelligent cognitive systems such as patient monitoring and assistive systems to ensure well being and improve the quality of life of Canadians and thus contribute to Canada's economy.
Program
Collaborative Research and Development Grants
Recipient
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
543845-2019
Name nom
Zulkernine, Farhana
Department département
Computing, School of
Organization id
29
Institution établissement
Queen's University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2020
Competition year année de concours
2019
Award amount
63000
Program id
CRDPJ
Program name en
Collaborative Research and Development Grants
Program name fr
Subventions de recherche et développement coopérative
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
1200
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Advancement of knowledge
Area of application fr
Avancement des connaissances
Research subject code
2805
Research subject group en
Artificial intelligence
Research subject group fr
Intelligence artificielle
Research subject en
Learning and inference theories
Research subject fr
Théories de l'apprentissage et l'inférence
Installment
inst:2 - 2
Partie
1
Nb partie
2
Application title
Learning Distributed Patterns from Multimodal Streaming Data_x000d_
_x000d_
_x000d_
_x000d_
Keyword
NSERC
Application summary
Human cognition depends on learning, correlating and recognizing data patterns using the sensory system, storing these patterns efficiently and matching them against newly perceived data for recognition and decision making. Pattern recognition has been explored for several decades but new challenges have evolved as thousands of connected digital devices have started generating terabytes of streaming data of different modalities such as audio, video, signal, and text. This data must be ingested, processed and correlated in parallel in real time for advanced digital perception and recognition to create machine intelligence. Large companies and internet cloud service providers are facing a serious challenge today to handle the data explosion problem. We need a system that can learn useful information from multiple types of streaming data in parallel for advanced cognition, store the knowledge, and reproduce the data streams as necessary while discarding unimportant data to optimize storage. The proposed research will develop a real time streaming data processing framework with novel deep learning models to learn and correlate distributed data patterns to enable machine cognition for a patient monitoring application. The novelty of the proposed research lies in 1) developing deep learning models and streaming data processing frameworks to extract regular, irregular and abnormal patterns from multimodal IoT, text and video data while addressing changes in learned patterns known as concept drifts, 2) creating a universal pattern representation and profiling technology to store data patterns as data profiles in a periodic event log rather than massive raw data, thus optimizing storage consumption and facilitating the development of machine learning models to reproduce data streams from the stored profiles, and 3) developing a pattern association model to correlate IoT, text and video data patterns for improved machine cognition. The proposed technology will serve as the backbone for developing a variety of intelligent cognitive systems such as patient monitoring and assistive systems to ensure well being and improve the quality of life of Canadians and thus contribute to Canada's economy.