Reference number : GC-2017-Q1-01899, amendment number : , agreement type : G, recipient legal name : Capretz, Miriam (The University of Western Ontario), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : The amount of data generated and recorded every day is growing at an unprecedented rate as a result of developments in Web technologies, social media, and mobile and sensor devices. This growth has led to massive data sets, which if properly analyzed, can provide valuable insights and knowledge. 
 
Data analytics aim to uncover useful information from data using techniques from statistics, machine learning, data mining, and other areas. Due to the size and diversity of the collected data sets, conducting data analytics today is challenging. First, it is typically insufficient to analyze data from a single domain; single domain data must be supplemented with data from other sources. For instance, customers' historical purchase records can be enriched with weather and social media information to create a richer analysis of shopping habits. Moreover, the sheer size of data poses challenges to existing analytics algorithms, which must be adapted or even redesigned to take modern run-time environments into consideration. Finally, visualizing large data sets and the progress of analytics processes is essential to users, but is not entirely supported by current tools. 
 
This research program aims to advance the field of cross-domain data analytics by devising a comprehensive architecture that can handle large heterogeneous data sets and provides users with rich and interactive visualizations of the analysis process. Hence, four main directions will be the focus of this research program. First, it will investigate cross-domain fusion techniques with the aim of integrating data and analytics processes from different domains and improving the quality and diversity of insights obtained. Second, this program will research transfer, ensemble, and reinforcement learning paradigms to enable them to work with large cross-domain data sets. Third, it will focus on creating a unified abstraction and accompanying run-time platfo rm that seamlessly fuses different learning processes and operates on large-scale data. Finally, this program will develop novel visualization techniques to present the knowledge induced during learning as well as the learning process itself. 
 
The proposed research program is expected not only to push the boundaries of data analytics research but also to assist organizations and individuals that are interested in obtaining insights from large-scale cross-domain data. By broadening the impact of data analytics, this program will assist Canadian companies to create a competitive advantage and will place Canada in a leadership position in this emerging field., program purpose french : The amount of data generated and recorded every day is growing at an unprecedented rate as a result of developments in Web technologies, social media, and mobile and sensor devices. This growth has led to massive data sets, which if properly analyzed, can provide valuable insights and knowledge. 
 
Data analytics aim to uncover useful information from data using techniques from statistics, machine learning, data mining, and other areas. Due to the size and diversity of the collected data sets, conducting data analytics today is challenging. First, it is typically insufficient to analyze data from a single domain; single domain data must be supplemented with data from other sources. For instance, customers' historical purchase records can be enriched with weather and social media information to create a richer analysis of shopping habits. Moreover, the sheer size of data poses challenges to existing analytics algorithms, which must be adapted or even redesigned to take modern run-time environments into consideration. Finally, visualizing large data sets and the progress of analytics processes is essential to users, but is not entirely supported by current tools. 
 
This research program aims to advance the field of cross-domain data analytics by devising a comprehensive architecture that can handle large heterogeneous data sets and provides users with rich and interactive visualizations of the analysis process. Hence, four main directions will be the focus of this research program. First, it will investigate cross-domain fusion techniques with the aim of integrating data and analytics processes from different domains and improving the quality and diversity of insights obtained. Second, this program will research transfer, ensemble, and reinforcement learning paradigms to enable them to work with large cross-domain data sets. Third, it will focus on creating a unified abstraction and accompanying run-time platfo rm that seamlessly fuses different learning processes and operates on large-scale data. Finally, this program will develop novel visualization techniques to present the knowledge induced during learning as well as the learning process itself. 
 
The proposed research program is expected not only to push the boundaries of data analytics research but also to assist organizations and individuals that are interested in obtaining insights from large-scale cross-domain data. By broadening the impact of data analytics, this program will assist Canadian companies to create a competitive advantage and will place Canada in a leadership position in this emerging field., agreement title english : Cross-Domain Data Analytics, agreement title french : Cross-Domain Data Analytics, agreement number : RGPIN, agreement value : 20000.0, agreement start date : 2017-05-10, 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