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Grant: Collaborative Research and Training Experience

Dalhousie University

$300,000.00

Training in Big Text Data (TRIBE)

FY 2016-17

Summary
We propose an industrial stream, advanced, collaborative graduate Training program in Big Text Data (TRIBE). On the one hand, Big Text represents additional challenges with respect to Big Data, as the information is unstructured and presented in (often simplified or incorrect) natural language. On the other hand, Big Text is ubiquitous in business, science, and everyday life - estimates indicate that 80% of Big Data is in fact text data. There is therefore a strong industrial interest in exploiting Big Text problems, and a significant need for Highly Qualified Personnel trained in Big Text Analytics techniques. Our proposal is based on five pillars: (1) a structured curriculum combining the necessary know-how, including ""soft skills"", in demand by industry, into a novel, specialized Graduate Certificate; (2) hands-on training in the form of an industrial internship for the students with companies such as IBM, Media Miser or Sales Force; (3) national and international student mobility and exchange; (4) a bilingual and multi-lingual application and training environment; (5) respect for privacy as a value instilled in our students and in our approach to Big Data. The combination of these pillars will provide unique, industrially-relevant graduate training of HQP in an area of unmet high demand in Canada and globally. The program involves three strong Canadian Computer Science departments at Dalhousie University (the lead applicant), Université de Montréal and Simon Fraser University. Students who will fulfil all the course and internship requirements will obtain a Graduate Certificate in Big Text Analytics. The Graduate Certificate will be an additional qualification granted to students who will satisfy the TRIBE requirements in the course of their Ph.D. or M.Sc. training. We expect this will attract additional HQP to TRIBE. We provide credible predictions of the strong industrial need for the profile of TRIBE graduates in the years to come, define an innovative curriculum providing technical skills, professional skills, and industrial and research components of the program. The applicants represent a strong team with significant experience in creating and leading inter-institutional, innovative graduate degrees.
Program
Collaborative Research and Training Experience
Location
Nova Scotia, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data

As published

Application id
448111-2014
Name nom
Matwin, Stan
Department département
Electrical Engineering and Computer Science , School of
Organization id
79
Institution établissement
Dalhousie University
Province en
Nova Scotia
Province fr
Nouvelle-Écosse
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2016
Competition year année de concours
2014
Award amount
300000
Program id
CREAT
Program name en
Collaborative Research and Training Experience
Program name fr
Programme de formation orientée vers la nouveauté, la collaboration et l'expérience en recherche
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
1078
Committee name en
CREATE Program
Committee name fr
Programme FONCER
Area of application code
1207
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Information, computer and communication technologies
Area of application fr
Technologie de l'information, des ordinateurs et des réseaux de communications
Research subject code
2710
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Information systems design
Research subject fr
Conception de systèmes d'information
Partie
3
Nb partie
6
Application title
Training in Big Text Data (TRIBE)
Keyword
NSERC
Application summary
We propose an industrial stream, advanced, collaborative graduate Training program in Big Text Data (TRIBE). On the one hand, Big Text represents additional challenges with respect to Big Data, as the information is unstructured and presented in (often simplified or incorrect) natural language. On the other hand, Big Text is ubiquitous in business, science, and everyday life - estimates indicate that 80% of Big Data is in fact text data. There is therefore a strong industrial interest in exploiting Big Text problems, and a significant need for Highly Qualified Personnel trained in Big Text Analytics techniques. Our proposal is based on five pillars: (1) a structured curriculum combining the necessary know-how, including ""soft skills"", in demand by industry, into a novel, specialized Graduate Certificate; (2) hands-on training in the form of an industrial internship for the students with companies such as IBM, Media Miser or Sales Force; (3) national and international student mobility and exchange; (4) a bilingual and multi-lingual application and training environment; (5) respect for privacy as a value instilled in our students and in our approach to Big Data. The combination of these pillars will provide unique, industrially-relevant graduate training of HQP in an area of unmet high demand in Canada and globally. The program involves three strong Canadian Computer Science departments at Dalhousie University (the lead applicant), Université de Montréal and Simon Fraser University. Students who will fulfil all the course and internship requirements will obtain a Graduate Certificate in Big Text Analytics. The Graduate Certificate will be an additional qualification granted to students who will satisfy the TRIBE requirements in the course of their Ph.D. or M.Sc. training. We expect this will attract additional HQP to TRIBE. We provide credible predictions of the strong industrial need for the profile of TRIBE graduates in the years to come, define an innovative curriculum providing technical skills, professional skills, and industrial and research components of the program. The applicants represent a strong team with significant experience in creating and leading inter-institutional, innovative graduate degrees.