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Grant: Discovery Grants Program - Individual

Saint Mary's University

$21,440.00

Predictive Business Analytics for Incomplete Data

EF 2019-20

Résumé
Business analytics refers to the process of continuous iterative exploration and investigation of relevant data to improve a company's performance. Business analytics relies on business insights and expert knowledge, analytics methodologies and technologies, as well as a huge amount of relevant data. A common type of business analytics is predictive analytics which involves exploiting business data to make predictions. Popular applications of predictive analytics include stock market prediction, customer lead generation, marketing campaign prediction, product bundling. The proposed project will focus on the theories and applications of predictive analytics.******Currently, predictive analytics typically employs traditional statistics, data mining, artificial intelligence and machine learning techniques for analyzing data. Many of these techniques generally require that there is no missing values in the data. However, real-life business data often contain missing values. For example, when data are collected from customers directly, many customers may be unwilling to provide all information because of privacy concerns. In the research literature, data with missing values are referred to as “incomplete data”. ******The primary objective of this project is to develop new predictive analytics methods for incomplete data. These proposed methods will be studied and validated through various business applications. Results from this project will significantly contribute toward the theories and applications of business analytics. We expect that the research results will be presented at international conferences and published in journals in the fields of computer science and business administration.**
Programme
Discovery Grants Program - Individual
Emplacement
Nova Scotia, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data

Tel que publié

Application id
RGPIN-2019-05787
Name nom
Wang, Hai
Department département
Finance, Information Systems, and Management Science
Organization id
85
Institution établissement
Saint Mary's University
Province en
Nova Scotia
Province fr
Nouvelle-Écosse
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2019
Competition year année de concours
2019
Award amount
21440
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
1507
Committee name en
Computer Science
Committee name fr
Sciences informatiques
Area of application code
802
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Information systems and technology
Area of application fr
Systèmes d'information et technologie de l'information
Research subject code
2717
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Modelling and simulation studies
Research subject fr
Études de modélisation et de simulation
Installment
inst:1 - 5
Partie
1
Nb partie
5
Application title
Predictive Business Analytics for Incomplete Data
Keyword
NSERC
Application summary
Business analytics refers to the process of continuous iterative exploration and investigation of relevant data to improve a company's performance. Business analytics relies on business insights and expert knowledge, analytics methodologies and technologies, as well as a huge amount of relevant data. A common type of business analytics is predictive analytics which involves exploiting business data to make predictions. Popular applications of predictive analytics include stock market prediction, customer lead generation, marketing campaign prediction, product bundling. The proposed project will focus on the theories and applications of predictive analytics.******Currently, predictive analytics typically employs traditional statistics, data mining, artificial intelligence and machine learning techniques for analyzing data. Many of these techniques generally require that there is no missing values in the data. However, real-life business data often contain missing values. For example, when data are collected from customers directly, many customers may be unwilling to provide all information because of privacy concerns. In the research literature, data with missing values are referred to as “incomplete data”. ******The primary objective of this project is to develop new predictive analytics methods for incomplete data. These proposed methods will be studied and validated through various business applications. Results from this project will significantly contribute toward the theories and applications of business analytics. We expect that the research results will be presented at international conferences and published in journals in the fields of computer science and business administration.**