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Grant: Research Tools and Instruments

York University

$96,554.00

Computing and Storage Infrastructure for Big Data Analytics

EF 2016-17

Résumé
In a world where data are growing at extraordinary rates, there is a huge demand for fast and effective analysis of big data to discover useful information. In the Data Mining Lab at York University, we are working on a number of research projects supported by NSERC for developing effective, efficient, and novel solutions for mining big data. Most of our projects involve industry collaboration where high volumes of data are used to develop solutions to practical problems in industry. This RTI grant will provide computational support required by these projects. We request a data storage server, a big memory server, and a number of workstations. The equipment will be used by the students and postdoctoral fellows working on the projects. These projects include (1) Online mining of big data streams using cloud computing, (2) Semi-supervised learning from data streams with emerging classes, (3) Novel geodesic and contour global optimization and its application to machine learning, (4) Data and visual analytics for decision marking in next generation news media, (5) Application of IBM platform solutions for data analytics problems, (6) An online integrated health risk assessment and recommendation tool, (7) Developing smart conversational systems, (8) Predicting forced migration using big data of news articles, (9) Developing a cloud-based platform for real-time facility analytics, and (10) Graph Mining and Exploration Algorithms. Our research has strong collaborations with Canadian industry, and covers various application areas that are involved with big data. We will develop novel and more effective technological solutions that can turn big data into beneficial actions for business. We believe such applications will result in product enhancements and new products for our partners, and will bring them significant competitive advantages globally.
Programme
Research Tools and Instruments
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data

Tel que publié

Application id
RTI-2017-00408
Name nom
An, Aijun
Department département
Computer Science and Engineering - Computer Science and Engineering
Organization id
38
Institution établissement
York University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2016
Competition year année de concours
2017
Award amount
96554
Program id
RTI
Program name en
Research Tools and Instruments
Program name fr
Outils et instruments de recherche
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
1607
Committee name en
Computer, Mathematical and Statistical Sciences (RTI)
Committee name fr
Sciences informatiques, mathématiques et statistiques (OIR)
Area of application code
801
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Computer software
Area of application fr
Logiciel
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
Partie
1
Nb partie
1
Application title
Computing and Storage Infrastructure for Big Data Analytics
Keyword
NSERC
Application summary
In a world where data are growing at extraordinary rates, there is a huge demand for fast and effective analysis of big data to discover useful information. In the Data Mining Lab at York University, we are working on a number of research projects supported by NSERC for developing effective, efficient, and novel solutions for mining big data. Most of our projects involve industry collaboration where high volumes of data are used to develop solutions to practical problems in industry. This RTI grant will provide computational support required by these projects. We request a data storage server, a big memory server, and a number of workstations. The equipment will be used by the students and postdoctoral fellows working on the projects. These projects include (1) Online mining of big data streams using cloud computing, (2) Semi-supervised learning from data streams with emerging classes, (3) Novel geodesic and contour global optimization and its application to machine learning, (4) Data and visual analytics for decision marking in next generation news media, (5) Application of IBM platform solutions for data analytics problems, (6) An online integrated health risk assessment and recommendation tool, (7) Developing smart conversational systems, (8) Predicting forced migration using big data of news articles, (9) Developing a cloud-based platform for real-time facility analytics, and (10) Graph Mining and Exploration Algorithms. Our research has strong collaborations with Canadian industry, and covers various application areas that are involved with big data. We will develop novel and more effective technological solutions that can turn big data into beneficial actions for business. We believe such applications will result in product enhancements and new products for our partners, and will bring them significant competitive advantages globally.