Reference number : GC-2017-Q4-01849, amendment number : , agreement type : G, recipient legal name : Viktor, Herna (University of Ottawa)|Viktor, Herna (Université d’Ottawa), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Business analytics tools provide intuitive, visual analytics solutions to allow organizations to discover the story_x000D_
that lives within their data. Increasingly, business intelligence solutions are utilized across a variety of_x000D_
industries, including, healthcare, financial services, retail and life sciences. In this setting, there is a need for_x000D_
machine learning solutions that may be used to optimize the resource-usage of analytics applications in_x000D_
cloud-based systems. There are several factors which need to be taken into consideration when aiming to find_x000D_
the optimal intelligent analytics application placement solutions. Firstly, the applications' behaviours in_x000D_
memory, and the CPU usages, are determined by a number of factors such as the amount of data included, the_x000D_
cardinality of the data, the complexity of the operations (e.g. aggregation or extensive sorting), the nature of the_x000D_
data model, as well as the sizes and locations of the data sources. In addition, the usage patterns of different_x000D_
types of users may vary considerably. For instance, push-button knowledge workers' usages may be far less_x000D_
resource-intensive than that of data scientists, who would typically perform advanced, ad hoc analytics. This_x000D_
project concerns the development of machine intelligence algorithms to address these issues, by building_x000D_
adaptive solutions to greatly improve placement policies of analytics applications across cloud-based platforms., program purpose french : Business analytics tools provide intuitive, visual analytics solutions to allow organizations to discover the story_x000D_
that lives within their data. Increasingly, business intelligence solutions are utilized across a variety of_x000D_
industries, including, healthcare, financial services, retail and life sciences. In this setting, there is a need for_x000D_
machine learning solutions that may be used to optimize the resource-usage of analytics applications in_x000D_
cloud-based systems. There are several factors which need to be taken into consideration when aiming to find_x000D_
the optimal intelligent analytics application placement solutions. Firstly, the applications' behaviours in_x000D_
memory, and the CPU usages, are determined by a number of factors such as the amount of data included, the_x000D_
cardinality of the data, the complexity of the operations (e.g. aggregation or extensive sorting), the nature of the_x000D_
data model, as well as the sizes and locations of the data sources. In addition, the usage patterns of different_x000D_
types of users may vary considerably. For instance, push-button knowledge workers' usages may be far less_x000D_
resource-intensive than that of data scientists, who would typically perform advanced, ad hoc analytics. This_x000D_
project concerns the development of machine intelligence algorithms to address these issues, by building_x000D_
adaptive solutions to greatly improve placement policies of analytics applications across cloud-based platforms., agreement title english : Intelligent Placement of Apps and Users on Qlik Analytics Engines, agreement title french : Intelligent Placement of Apps and Users on Qlik Analytics Engines, agreement number : EGP, agreement value : 25000.0, agreement start date : 2018-02-07, 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