Creativity to learn,
understand and valorize data.
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Université Laval BDRC
The field of big data has been recognized as a priority area of development and highly promising by the Faculty of Science and Engineering and Université Laval.
Indeed, Big Data analysis is a challenge for a growing number of scientific disciplines considerating it merges problems of many domains to the same playing field.
With the contribution of researchers from five faculties, the Big Data Research Center (BDRC) is a catalyst for these multidiciplinary approaches. It intends to become a pivotal player in the field of massive data.
Our research areas
BioInformatics
The field of bioinformatics faces a major problem: the researchers produce phenomenal amounts of data.
Since Bioinformatics covers "every aspect of computer science uses in the management, storage, analysis, processing, organization, comparison and dissemination of data relating to all biological sciences " (OQLF, 2005), the task is massive.
The BDRC is involved in multiple research projects related to important issues in bioinformatics :
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Mathematical modeling and computational methods in biology.
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The increase in analytical capacity faces an ever-growing quantity of produced data by the new high-speed technologies, like new generation sequencers, spectrometry mass, high throughput transcriptomics, and structural biology.
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Algorithms development to analyze and structure quantity ever more impressive experimental data created in laboratory.
Unstructured Data
The use of heterogeneous datasets, or unstructured data, represents a major research challenge in processing big data. Different data can come under more than one form, which requires a complex model of data fusion.
The BDRC is actively involved in research projects related to important issues in non or partially structured data :
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Multimodal model development, a data fusion model which makes it possible to exploit, for example, a set of image data accompanied by a set of textual data, corresponding to the textual descriptions of these images.
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Approaches using machine learning, including deep learning which represents the state of the art in big data learning techniques.
Confidentiality, ethics and social acceptability
Confidentiality, integrity and respect for privacy are major concerns and must be taken into account. Indeed, all aspects of our daily lives are now massively managed by technological tools, which is critical in areas such as health, the economy or national security. Remote and heterogeneous sites must therefore have a particularly strict access control.
The MDRC collaborates on research projects whose subjects touch important issues in data privacy :
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Improving confidentiality and integrity through access control mechanisms.
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Application information flow control to have a better control over the way information is used once access is granted.
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Methods development protecting the privacy of individuals when analyzing and sharing their personal information.