The Big Data Research Center
Big data analysis is both an opportunity and a challenge for a growing number of research fields. Increasingly a number of fields are realising that their data streams are a potential source of revenues and exploration. The Big Data Research Centre (BDRC) draws on the contributions of researchers from five faculties to make it a catalyst for such multidisciplinary approaches. The Centre is thus poised to take a leading role in the field of big data analytics.
The CRDM aspires to become the premier research centre for big data processing in Quebec, Canada, and internationally by 2021. We hope to become a key partner of Université Laval, especially for major big data projects.
The CRDM seeks to tackle major societal issues by involving and bringing together leading actors in computer science, statistics, bioinformatics, medicine, forestry, and administration, both on campus and at affiliated hospitals. We will achieve this mission by training highly qualified data management and analysis personnel, and recruiting gifted researchers in the field of big data.
The CRDM promotes the following principles in all its initiatives:
Sharing and collaboration,
Inquiry, excellence, and intellectual rigour,
Innovation and design,
Transfer and development of the knowledge, expertise, and excellence of all Centre researchers.
The BDRC is a multidisciplinary centre, with five key objectives to realise on a timeframe of five years:
Establish a single portal at Université Laval for research groups and private companies that generate big data and want to use it for R&D. The centre’s purpose in so doing is to enrich fundamental knowledge and apply this knowledge to today’s realities.
Train staff qualified to address the challenges of big data in the fields where their support is needed, such as informatics, biology, medical research, forestry, administration, statistics, and engineering.
Foster local, national and international collaborations among researchers to facilitate access to government and private sector funding for major projects.
Encourage training in and proper use of existing software but above all develop new targeted data analysis processes and algorithms to address observed research problems.
Promote interdisciplinarity by bringing all necessary resources on board to tackle the major societal issues.