Reference number : GC-2017-Q4-00916, amendment number : , agreement type : G, recipient legal name : Khoury, Richard (Université Laval), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Engage Plus Grants for universities, program name french : Subvention d'engagement partenarial Plus pour les universités, program purpose english : The Canadian insurance company La Capitale Assurances Générales has collected over time several massive_x000D_
datasets about their customers, insurance plans, and various insured items. They are now looking for ways of_x000D_
efficiently combining these datasets together, enrich it with online data, and inferring new knowledge about_x000D_
customers from it, with the long-term goal of mining this data to learn more information about their customers'_x000D_
habits and lifestyles, in order to customize their insurance coverage to each individual customer's needs. In the_x000D_
original Engage grant, we designed learning algorithms to discover this knowledge from their data, and_x000D_
implemented a simple database to centralize the information into one source. In this follow-up Engage Plus_x000D_
grant, we will explore more advanced methods focusing on resolving the issue of the imbalanced class data in_x000D_
their dataset, an issue that was discovered during the work on the Engage grant, and we will improve the_x000D_
database system. The insurance industry is a multi-billion dollar industry, and the data management tools and_x000D_
knowledge discovery algorithms we will develop for La Capitale Assurances Générales will give Canadian_x000D_
companies an important edge in this competitive international marketplace., program purpose french : The Canadian insurance company La Capitale Assurances Générales has collected over time several massive_x000D_
datasets about their customers, insurance plans, and various insured items. They are now looking for ways of_x000D_
efficiently combining these datasets together, enrich it with online data, and inferring new knowledge about_x000D_
customers from it, with the long-term goal of mining this data to learn more information about their customers'_x000D_
habits and lifestyles, in order to customize their insurance coverage to each individual customer's needs. In the_x000D_
original Engage grant, we designed learning algorithms to discover this knowledge from their data, and_x000D_
implemented a simple database to centralize the information into one source. In this follow-up Engage Plus_x000D_
grant, we will explore more advanced methods focusing on resolving the issue of the imbalanced class data in_x000D_
their dataset, an issue that was discovered during the work on the Engage grant, and we will improve the_x000D_
database system. The insurance industry is a multi-billion dollar industry, and the data management tools and_x000D_
knowledge discovery algorithms we will develop for La Capitale Assurances Générales will give Canadian_x000D_
companies an important edge in this competitive international marketplace., agreement title english : Insurance consumer information data lake, agreement title french : Insurance consumer information data lake, agreement number : EGP2, agreement value : 12500.0, agreement start date : 2018-03-07, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada