Hash Code started in 2014 with just 200 participants from France. In 2017,
more than 26,000 participants from across Europe, the Middle East and Africa
took part in the competition. You can take a look at the problems and
winning teams from past editions of Hash Code below.
Hash Code 2017, Final Round
Who doesn't love wireless Internet? Millions of people rely on it
for productivity and fun in countless cafes, railway stations and
public areas of all sorts. For many institutions, ensuring
wireless Internet access is now almost as important a feature of
building facilities as the access to water and electricity.
Typically, buildings are connected to the Internet using a fiber
backbone. In order to provide wireless Internet access, wireless
routers are placed around the building and connected using fiber
cables to the backbone. The larger and more complex the building,
the harder it is to pick router locations and decide how to lay
down the connecting cables.
Hash Code 2017, Online Qualification Round
Have you ever wondered what happens behind the scenes when you
watch a YouTube video?
As more and more people watch online videos
(and as the size of these videos increases), it is critical that
video-serving infrastructure is optimized to handle requests
reliably and quickly.
This typically involves putting in place cache servers, which
store copies of popular videos. When a user request for a
particular video arrives, it can be handled by a cache server
close to the user, rather than by a remote data center thousands
of kilometers away. Given a description of cache servers, network
endpoints and videos, along with predicted requests for individual
videos, decide which videos to put in which cache server in order
to minimize the average waiting time for all requests.
Hash Code 2016, Final Round
A satellite equipped with a high-resolution camera can be an
excellent source of geo imagery. While harder to deploy than
a plane or a Street View car, a satellite — once launched —
provides a continuous stream of fresh data.
Terra Bella is a
division within Google that deploys and manages
high-resolution imaging satellites in order to capture
rapidly-updated imagery and analyze them for commercial
customers. With a growing constellation of satellites and
a constant need for fresh imagery, distributing the work
between the satellites is a major challenge. Given a set of
imaging satellites and a list of image collections ordered by
customers, schedule satellite operations so that the total
value of delivered image collections is as high as possible.
Hash Code 2016, Online Qualification Round
The Internet has profoundly changed the way we buy things,
but the online shopping of today is likely not the end of
that change; after each purchase we still need to wait multiple
days for physical goods to be carried to our doorstep. Given a
fleet of drones, a list of customer orders and availability of
the individual products in warehouses, schedule the drone
operations so that the orders are completed as soon as possible.
Hash Code 2015, Final Round
Project Loon aims to bring universal Internet access
using a fleet of high altitude balloons equipped with
LTE transmitters. Circulating around the world, Loon
balloons deliver Internet access in areas that lack
conventional means of Internet connectivity. Given the
wind data at different altitudes, plan altitude
adjustments for a fleet of balloons to provide Internet
coverage to select locations.
Hash Code 2015, Online Qualification Round
For over ten years, Google has been building data
centers of its own design, deploying thousands of
machines in locations around the globe. In each of these
of locations, batteries of servers are at work around
the clock, running services we use every day, from
Google Search and YouTube to the Judge System of Hash
Code. Given a schema of a data center and a list of
available servers, your task is to optimize the layout
of the data center to maximize its availability.
Hash Code 2014, Final Round
The Street View imagery available in Google Maps is
captured using specialized vehicles called Street View
cars. These cars carry multiple cameras capturing
pictures as the car moves around a city. Capturing the
imagery of a city poses an optimization problem: the
fleet of cars is available for a limited amount of time
and we want to cover as much of the city streets as