Liam Kopp BSI Application

A unified dataset from disparate data sources is the ideal solution for many organizations and data teams alike. In this project, we propose to create an automated methodology using a combination of traditional SQL, graph and vector databases to combine and store data along with developing modeling techniques using machine and deep learning to automate the ingestion process. By combining multiple data storage solutions and automated data ingestion, we aim to reduce effort required to consolidate data, simplify data access and increase the variety data analysis at organizations using existing and new data sets.

Faculty Supervisor:

Sandy Staples

Student:

Partner:

Data Loft Technologies Inc.

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Queen's University

Program: