Our inspration is the famous mathematical problem of the "Seven Bridges of Königsberg", where we want to visit each location (bridge) only once.

What it does

Given three main constrains which are: time, money and the topics a person is interested in, the system determines which places in the area (museums, monuments, places of interest in general) are the best fit to our preferences - therefore:

  • we save time by visiting the most relevant places to us first
  • we save cash by spending within the set limit

How we built it

First of all we split the tasks, so each of us could work on what inspired him the most and at the same time get the work done.

We began by setting up the Amazon AWS server, that is the core of our service, and then we wrote the algorithm and the API for our service and installed it in the cloud. We continued by developing the Android application that interfaces with the API and gets notified by the beacons when one of them is in the area.

Finally we used the Twitter API to get posts from any given timeline and processed that information by using Jaccard indices to find out the similarity between twitter activity and data about the available locations to visit. Of course if the user uses a twitter handle that is not his own the results will differ. Example: To a person who has many posts about rock music on his timeline it is more probable to be advised to visit Hard Rock Cafe, instead of Sagrada Familia.

Challenges we ran into

At first it was quite tricky to get the text analysis right by accessing the Wolfram Cloud API from python. We got some mentoring from the Wolfram staff present at the event, and then we went to work again on that part of the project. After some time we've succeeded and managed to use a twitter handle of any given user to get the text content from his timeline and analyze it to see which of our travel options may fit him best.

Accomplishments that we're proud of

  • Creating a greedy algorithm that solves our problem.
  • Being able to use Wolfram for text analysis purpose and making our own API to use Wolfram Cloud embedded code
  • Creating interaction with BLE beacons
  • Have an Amazon AWS server up and running our service
  • Finishing what we set ourselves to do.

What we learned

We learned how to use the Wolfram API and Amazon AWS for performing computation in the cloud. Since our project is also hardware-based we learned how to interact with BLE beacons to create physical web and to create interaction with mobile devices.

What's next for DriveFlyTeleport

Add more beacons and more places to the system. Use AWS machine learning tools to make use of the data so we can provide a higher quality service to our users.


We registred the following domain name by using the service:

it's all a pretty long sentence: Don't drink and drive, don't smoke and fly, take LSD and teleport.

We found it quite funny and named our project DriveFlyTeleport for that reason.

you can find more about the domain at:

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