Grant: Applied Research and Development Grants - Level 1
Saskatchewan Polytechnic
$25,000.00
Asset Data Amalgamation and Analysis for Agricultural Platform
EF 2021-22
Résumé
In partnership with Mera Development Corp (MDC), Saskatchewan Polytechnic (SP) is developing a sub system for MDC's Asset Data Management (ADaM) platform. Specifically, the proposed applied research will develop a system capable of performing data aggregation and migration across multiple corporate databases while maintaining or introducing relationships between the data points. The system infrastructure will collect, scan and import data to a NoSQL database (Lake). That aggregated data will be used to support machine learning (ML) and AI algorithms to provide end users with analysis of their data. The Engage phase of the project will focus on data migration and preprocessing. This will involve: (a) building the database (DB) and user interface, (b) determine how to best migrate the data from different types (brands) of database (c) and selection of the appropriate preprocessing techniques for that data. This sets the foundation to allow individuals without extensive data expertise to migrate and preprocess their data. The application will benefit anyone who needs to take more control over their data aggregation strategies and aid in the application of ADaM functionality to improve and optimize their business and manufacturing processes.
Programme
Applied Research and Development Grants - Level 1
Bénéficiaire
Emplacement
Saskatchewan, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
570644-2021
Name nom
Blum, Susan
Department département
Head Office
Organization id
44957
Institution établissement
Saskatchewan Polytechnic
Province en
Saskatchewan
Province fr
Saskatchewan
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2021
Competition year année de concours
2021
Award amount
25000
Program id
CARD1
Program name en
Applied Research and Development Grants - Level 1
Program name fr
Subventions de recherche et développement appliquée - niveau 1
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
1561
Committee name en
Applied Research and Development Grants
Committee name fr
Subventions de recherche et développement appliquée
Area of application code
500
Area of application group en
Manufacturing processes and products
Area of application group fr
Procédés et produits industriels
Area of application en
Manufacturing processes and products
Area of application fr
Procédés et produits industriels
Research subject code
2711
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Database management
Research subject fr
Gestion de bases de données
Installment
inst:1
Partie
1
Nb partie
1
Application title
Asset Data Amalgamation and Analysis for Agricultural Platform
Keyword
NSERC
Application summary
In partnership with Mera Development Corp (MDC), Saskatchewan Polytechnic (SP) is developing a sub system for MDC's Asset Data Management (ADaM) platform. Specifically, the proposed applied research will develop a system capable of performing data aggregation and migration across multiple corporate databases while maintaining or introducing relationships between the data points. The system infrastructure will collect, scan and import data to a NoSQL database (Lake). That aggregated data will be used to support machine learning (ML) and AI algorithms to provide end users with analysis of their data. The Engage phase of the project will focus on data migration and preprocessing. This will involve: (a) building the database (DB) and user interface, (b) determine how to best migrate the data from different types (brands) of database (c) and selection of the appropriate preprocessing techniques for that data. This sets the foundation to allow individuals without extensive data expertise to migrate and preprocess their data. The application will benefit anyone who needs to take more control over their data aggregation strategies and aid in the application of ADaM functionality to improve and optimize their business and manufacturing processes.