The Randomness of Opportunities
Hackathons can be stressful experiences, as a group of students from Eden Prairie, Minnesota recently found out. The students, belonging to the Futurist Academy, a community dedicated to fostering the development of young people by focusing them on applying emerging technologies to reshape the future, had only eight hours to solve an important problem.
The students were challenged by the organizers of the hackathon to select an important issue affecting individuals or groups of people and determine a solution to the issue. After deep thought, and a lot of whiteboard discussion, the team from the Futurist Academy had its “light bulb” moment.
Here’s how Rama Pitchala, a member of the Eden Prairie Futurist Academy, described the team’s revelation. “Just for a moment, think about the randomness of opportunities that you may experience throughout your life – in many instances, the fact that these opportunities appeared at all may have been mere lucky rolls of a dice. Moreover, you may have missed opportunities which could have profoundly shaped your present situation – in a few instances, we hear about these opportunities too late, but for most, we never know about them.”
For instance, it was mostly luck that the Futurist Academy team was able to participate in the hackathon at all – and this made the students very aware of how a chain of events introduces uncertainties to noticing, and acting on, opportunities that could be life-affecting. The team was, consequently, motivated to find a way to minimize these uncertainties – and took inspiration from existing technologies used by top companies such as Netflix and Google.
Making Opportunities Less Stochastic
To resolve these inefficiencies and turn chance into planned, the team took inspiration from existing technologies used by top companies such as Netflix and Amazon that use graph analysis to make recommendations. Netflix recommends interesting movies to someone by identifying similar people who enjoyed the same film and also enjoyed other movies. Amazon utilizes a similar approach when providing suggestions for additional other products, or bundles of products.
Rather than suggest movies and products, the team from the Futurist Academy’s idea consisted of an intelligent engine able to recommend resources that may improve the user’s life, enhance the user’s knowledge, and connect the user with like-minded people.
Offering Opportunities with TigerGraph
Ten days prior to participating in the hackathon, the high school students had learned about TigerGraph and had successfully studied for, and passed, our TigerGraph Associate certification exam.
According to Pitchala, “With TigerGraph we are able to build an intelligent entity that looks in the best interest of its users, and opportunities will no longer be at the erratic, inconsistent hands of probability but at the calm hands of calculation.” Continued Pitchala, “TigerGraph was our secret weapon”.
The team developed a graph-based model, geared towards high school and college students, that recommends meetups, scholarships, and other potentially life-affecting information. Using TigerGraph, the team from the Futurist Academy was able to scale their data easily. The engine provided the ability to look at each school, factoring in the interests of students, constructing an ever-better graph and, thereby improving the accuracy of the recommender model.
In addition, with built-in functionality and rest endpoints, the team was able to quickly build the recommender system as a query and expose the query as a rest endpoint that can be conveniently accessed through an intuitive user interface via an API call.
Finally, TigerGraph’s combination of speed, low storage use, ability to retain relationships between entities, and ability to provide greater insights into the data through deeper hops or connections solidified its role as our primary choice.