Research and Design of In-Memory Database for Vehicles’ Telematics Based on IoT Big Data Environment

Road safety affects everyone, not just Geotab customers. With several years of driving and environmental data from over 2 million vehicles, we have an opportunity to make our customers safer, as well as our communities and cities. To reduce accidents, we need to understand both the driving behavioral patterns that are predictive of accidents and the environmental factors involved. Thus, the data infrastructure should be capable of processing a series of real-time video and telematics data, as well as time-series historical records generated from existing machine learning models to respond to real-world incidents with low latency. However, data communication between the data warehouse and the models/applications is inefficient, an in-memory database is then considered to improve the data communication efficiency. Due to the nature of driving data that it is changing all the time, the structure of the database should be carefully designed and analyzed.

Faculty Supervisor:

Ben Liang

Student:

Partner:

Geotab Inc

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services; Transportation and warehousing

University:

University of Toronto

Program: