IoT analytics by NUS students

Between January and April 2020, a LynxKite team (Gabor, Marton and Fai) worked with a group of undergraduate students, Victoria Teo, Ann Qi Neo, Jian Lai Ng and Benita Neo, from The National University of Singapore (NUS) on their Capstone Project. The project requires the students to complete a real-world business analytics project based on the principles they learned from various modules in their business analytics program.

The team was intrigued by the growing volume of IoT data and aimed to use graph theory and machine learning to generate insights and solve problems for businesses like Bay Wheels.

As part of the deliverables, the team used LynxKite to convert the IoT data into graphs, explored the graph and derived graph features like centrality, in/out degrees, etc. With LynxKite, they were able to enrich the recommendations concluded from this project. Part of their capstone project is published on Medium..

Also find out what they have to say about LynxKite:

“I like the Jupyter notebook and the cleanliness of the LynxKite UI, easy to manoeuvre around. I also like that it feels like a playground where I can connect things together and make something out of it. I also liked that features can connect to others in a many to many way (not just limited to one arrow each function)…”
Ann Qi

“I like how you can drag the pop-up boxes around. Snapshots are pretty useful…”

For anyone interested in Graph Theory / Network Analysis, you can now use LynxKite for free under the AGPL license.

NUS + LynxKite team