The challenge from FairTIQ and our motivation for applying our knowledge in Computer Science on the real world with specific data.
What it does
How we built it
It uses neo4j on the cloud for the graph database and Python for its core.
Challenges we ran into
We encountered several difficulties trying to deal with the given problem. The main one was the huge amount of data given and its structure complexity. For example, we first tried to load the full dataset into the code which proved to be infeasible and we found a way to achieve it. But not only that, we also tried to ran the database on the cloud which we accomplished after trying IBM Cloud, Google and AWS.
Accomplishments that we're proud of
Managing huge amounts of data not saved on the program's memory and making them persistent for several executions efficiency. We are also proud of our increase in Python skills and in our capacity to learn neo4j from scratch.
What we learned
To manage the massive amounts of data in a fast and practical way with our null knowledge of big data.
What's next for GraphTIQ
To reach a more optimal solution based on the work already done. Moreover, we also would like to have a better way to provide the data and to visualize it as we already have the information needed to show it like a geographical map. Not only that, but we could also increase our data scope based on the GTSF information and more time.