From personal frustrating experience in unorganized airports as well as sympathy for our PERSONA airport operator SALLY, we asked ourselves how to use historical data and combine it with realtime data to make SALLY's and our life easier.

What it does

Our solution is a structured dashboard on realtime and predictive passenger figures that visualizes locations of congestion. It integrates connected things and supports therefore resource allocation, decision making and maintenance.

How we built it

In order to ramp up quickly, we created an initial user story. While evaluating data and technologies for this story, our team developed draft components. Through continuous improvement of the story, the components also advanced significantly. By integrating these components steps by step, we reached the solution and therefore satisfy our users' needs.

Challenges we ran into

We've experienced various challenges in our project. Examples include connectivity issues to databases and the aggregation of data where more detailed information like e.g. exact timestamps would have been valuable. Our efforts to implement an anonymous but user-specific tracking solution has been facilitated by bluetooth with Intel Edison. Scaling this approach according to our requirements would require further efforts.

Accomplishments that we're proud of

Our fast and hassle-free collaboration in a randomly matched team with diverse skill sets. It enabled us to form ambitious goals and execute on complex data and APIs efficiently. Resulted in a complete working environment and FUN collaboration.

What we learned

Great teamwork leaves freedom for individual ideas and capabilities but also aligns with user-driven solutions. From an operational perspective, we experienced that big data require thorough preparation. Personal data appear most suited for predictive process management whereas aggregated process-level data serves mainly monitoring purposes.

What's next for Sally

Automatization of data collection and preparation. Recommendation of best practice solutions based on historical data. Generalize solution.

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