Big Data Analytics for Smart City Infrastructure - F23

CIVI 691K
Closed
Concordia University
Montreal, Quebec, Canada
Associate Professor
3
Timeline
  • September 30, 2023
    Experience start
  • December 19, 2023
    Experience end
Experience
1 projects wanted
Dates set by experience
Preferred companies
Anywhere
Any company type
Any industries

Experience scope

Categories
Information technology Data analysis Operations Project management
Skills
machine learning data analysis sustainability rapidminer infrastructure engineering
Learner goals and capabilities

Student-consultants will analyze city data sets (normally available through open city portals, etc.) through state-of-the-art Machine Learning and Data Mining technologies, to: identify trends, and/or create predictive models. Their models are used to create solutions for infrastructure sectors (transportation, building, energy, urban water/drainage, etc.) which can be deployed using digitalization in smart cities.


Learners

Learners
Undergraduate
Any level
50 learners
Project
75 hours per learner
Learners self-assign
Teams of 4
Expected outcomes and deliverables

The student will deliver the following:

  1. A 10 - 15 page report, explaining their problem statement and objectives, the methods they followed, The model they developed, and Their results;
  2. A 10-15 minute presentation
  3. The model(s) developed (in form of RapidMiner processes), as well as the pre-processed data they used

Project timeline
  • September 30, 2023
    Experience start
  • December 19, 2023
    Experience end

Project Examples

Requirements

Student-consultants will analyze urban data sets using data mining and machine learning technologies to improve city efficiency, sustainability and resilience.

Some past project examples include:

  • Road Condition Assessment through Data Mining
  • Real Estate Price Forecast through Data Mining
  • Predictions for Available Parking Spots in Various North American Cities
  • Analysis of Road Safety and Road Accidents
  • Improving Building Thermal Comfort and Energy Performance using Machine Learning
  • Analysis and Prediction of Energy Consumption Behavior at Building, District and City Level

Additional company criteria

Companies must answer the following questions to submit a match request to this experience:

provide a dedicated contact who is available to answer periodic emails or phone calls over the duration of the project to address students' questions.

be available for a quick phone call with the instructor to initiate your relationship and confirm your scope is an appropriate fit for the course.

Do you agree to provide data so the students can work on them?