Building a Scalable Data Platform Infrastructure for Smart Fleet and Urban Analytics

Geotab creates hardware that it installs into customer vehicles. Telematic and video data from this hardware is collected and aggregated by its data platform where it can be used for analysis and machine learning endeavours to provide insights to
customers. Large volumes of data are generated and must be ingested efficiently and accurately as well streamed to customers in real time. The data, in particular video data, must also be stored in an efficient manner. Lastly the platform needs to be scalable as the quantity of data continues to increase. The data platform currently has methods to handle these needs, but Geotab is a rapidly growing company. New techniques are needed to ensure the data platform remains performant and reliable.
Successful research will benefit Geotab by allowing it to further expand its operations while ensuring low latency, low cost, and reliability. Society as whole will benefit due to Geotab’s software allowing its customers to manage their fleets of vehicles more
efficiently, safely, and sustainably.

Faculty Supervisor:

Eyal de Lara;Qizhen Zhang

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: